July 2019 A publication by JULY 2023 August 2023 Ilke Demir The mother of FakeCatcher
Computer Vision News 2 Trusted Media One of the consequences of AI democratization and the growing emergence of generative models is the rise of deepfakes. Through its work to determine the authenticity of media content, Trusted Media aims not simply to identify artifacts of fakery, including broken hands and symmetry issues, but to answer a deeper question: Is there an inherent watermark in being human? “The first thing we look at is blood,” Ilke tells us. “When our heart pumps blood, it goes to our veins, and they change color. That color change is called photoplethysmography (PPG). We collect those PPG signals from the face, look at their temporal, spectral, and spatial correlations, and create PPG maps. On top of those, we train a deep neural network to classify them into fake and real videos!” This technology is called FakeCatcher, one of several deepfake detectors developed by the team. Others examine whether eye gaze remains consistent over time and whether the motion in a video aligns with natural human movements. Then there is multimodal detection, such as exploring correlations between head movements and voice changes. Deepfakes extend into scene and Ilke Demir is a Senior Staff Research Scientist at Intel Labs, leading the Trusted Media team, working on manipulated content detection, responsible generative AI, and media provenance. Real-time Deepfake Detection Platform
3 Computer Vision News object manipulation and even satellite imagery. To address this, the team has built a detector that breaks down large satellite images into smaller patches, enhancing their resolution using super-resolution techniques and utilizing a multihead attention transformer network to extract textures and features to determine their authenticity. “We have very high accuracy rates, especially for multimodal and multidomain approaches,” Ilke reveals. “FakeCatcher has 96% accuracy, our multimodal detector has over 98%, and our satellite imagery detector has 99.62%. It’s getting better and better!” Ilke compares this work to an arms race–as the generators improve, so do the detectors, but then the generators improve again, and so on. It is an endless game of cat and mouse. However, she assures us the detectors are always one step ahead of any new generator, using authenticity signatures and priors in the data rather than conforming to fakery. We have to ask: What could go wrong? “I would say people can go wrong!” Ilke laughs. “Instead of using our technology to enhance decisionmaking, if people try to use it as the absolute decision-maker for validating fakes, then that’s bad. We’re not saying any of the detectors are 100% perfect. That’s why we provide so many different detectors. We don’t want to decide for you. The biggest risk is always humans because our systems are mostly deterministic, but humans arenot.” Trusted Media has also been building trust metrics around technical systems to enhance interpersonal trust, societal trust, Ilke Demir
Computer Vision News 4 people’s trust in systems, and systems’ trust toward people. To this end, it conducts user studies, collaborates with social scientists, engages with customer groups, and utilizes its systems to investigate various trust-related factors. One of its main objectives is to minimize the risk in the current climate of marginalizing humans in this equation. Regular readers may recognize Ilke from her Women in Computer Vision feature in 2017. Since then, her career has gone from strength to strength. As the self-declared “mother of FakeCatcher”, her passion for the technology shines through as she talks about it, and she takes pride in the fact that it belongs to her rather than any corporation. “My background is in proceduralization, which is computer graphics, computer vision, and machine learning to find interpretable representations from 3D data,” she explains. “I’ve been looking at priors, distributions, and generative models my entire life –20 years of research. When deepfakes were rising, I thought, ‘Generative models also generate deepfakes, and generative models have those priors. Can we find some human prior in data to depend on in that generated content?’ I was also working on human understanding in virtual reality. At that point, I was like, there are some human priors, and machine learning can predict humans, so we can build something...” Ilke then saw an MIT paper about PPG signals, looking at blood flow from videos, and realized its potential for analyzing deepfakes. Alongside her colleague Umur Aybars Ciftci, they began running experiments on the data and proving why PPG works. FakeCatcher soon Trusted Media
5 Computer Vision News became accomplished at finding every deepfake. The team at Intel super-optimized it using OpenVINO, VNNI, AVX, and all the Intel AI accelerators to make it super realtime on Intel Xeon processors. Up to 72 concurrent FakeCatcher streams can be run on one machine. Responsible generative AI is another research area for the Trusted Media team. Can deepfakes be responsible and used for good? Can inherently ethical and responsible generative AI be built with design priors for the architecture rather than trying to patch it afterward? “If we start with a responsible generative model, then everything else will follow through because we aren’t leaving room for disinformation, misinformation, false data, or impersonation,” Ilke points out. “We’re trying to counter all the harmful aspects of generative AI by design, network architecture, loss choices, training data, and watermarking.” Trusted Media’s other research domain is media provenance, which involves establishing the authorship of media content. It addresses the issue of false ownership claims by providing crucial information about the origin, creator, creation process, purpose, and consent behind the media. The goal is to embed this information directly into the content, like a fingerprint. Even if a generative model is used to create the person in the video, it is acceptable if it is the consented and genuine version. Various information-theoretic methods, such as authentication and watermarking, can embed provenance Ilke Demir “Our Real-time Deepfake Detection Platform is the flagship product supporting our corporate responsibility towards the world!”
Computer Vision News 6 information intosynthetic data. We have to ask Ilke the million-dollar question: Why is a company that exists to make money investing so much time and energy into this endeavor? Is it doing humankind a favor, or is there another opportunity here? “That’s a wonderful question,” she responds. “There are several value captures coming from Trusted Media. These algorithms are superoptimized to work on Intel hardware, including Intel Xeon and VPU, which supports more platform and hardware sales. The OpenVINO team is doing wonders. They were running Stable Diffusion with one frame per second. Wow! My team’s detectors and responsible generative AI approaches follow that path. But all big corporations have some corporate social responsibility. At Intel, we call it RISE initiatives and our Real-time Deepfake Detection Platform is the flagship product supporting our corporate responsibility towards the world. We support trust and human-centric and responsible AI through our products. These are key values for Intel!” There is also a critical humanitarian angle to Trusted Media’s work. It collaborates with human rights organizations, nonprofits, and civil organizations to combat misinformation and disinformation in cases where high-risk and highimpact deepfakes emerge. To stop the spread, it strives to provide immediate results and accuracy to these organizations, ideally within thehour. A crucial aspect is providing Trusted Media
7 Computer Vision News information to people in emergency situations in war zones. “If you remember, there was the Zelensky deepfake giving misinformation about the Russian invasion,” she recalls. “We may think it’s fake, but people on the ground might not know that. They see their president saying something and believe it. We can help everyone decide if what they see is the truth!” And that is AI for good. “Absolutely, yes!” Ilke Demir Computer Vision News Editor: Ralph Anzarouth Publisher: RSIP Vision Contact us Give us feedback Free subscription Read previous magazines Copyright: RSIP Vision All rights reserved Unauthorized reproduction is strictly forbidden. Follow us: Ralph’s photo on the right was taken in lovely, peaceful and brave Odessa, Ukraine. Ilke was already awesome in 2017 (and before) – If you missed that interview, don’t miss this link!
Computer Vision News 8 Congrats, Doctor Carlos! Carlos Rodríguez - Pardo did an industrial PhD at Universidad Rey Juan Carlos, which was fully funded by SEDDI, a startup based in Madrid focused on digitizing the fashion industry. His PhD was supervised by awesome Elena Garcés. The focus of his thesis is to develop deep learning based methods for digitizing materials, inverse graphics, and encoding radiance for virtual scenes. Congrats, Doctor Carlos! Realistic virtual scenes are becoming increasingly prevalent in our society, with a wide range of applications in areas such as manufacturing, architecture, fashion design, and entertainment, including movies, video games, and augmented and virtual reality. Generating realistic images of such scenes requires highly accurate illumination, geometry, and material models, which can be time-consuming and challenging to obtain. Traditionally, such models have often been created manually by skilled artists, but this process can be prohibitively time-consuming and costly. Alternatively, real-world examples can be captured, but this approach presents additional challenges in terms of accuracy and scalability. Moreover, while realism and accuracy are crucial in such processes, rendering efficiency is also a key requirement, so that lifelike images can be generated with the speed required in many real-world applications. One of the most significant challenges in this regard is the acquisition and representation of materials, which are a critical component of our visual world and, by extension, of virtual representations of it. However, existing approaches for material acquisition and representation are limited in terms of efficiency and accuracy, which limits their real-world impact. To address Neural Networks for Digital Materials and Radiance Encoding Carlos with Elena at CVPR
these challenges, data-driven approaches that leverage machine learning may provide viable solutions. Nevertheless, designing and training machine learning models that meet all these competing requirements remains a challenging task, requiring careful consideration of trade-offs between quality and efficiency. In my thesis, we propose novel learning-based solutions to address several key challenges in physically-based rendering and material digitization. Our approaches leverage various forms of neural networks to introduce innovative algorithms for radiance encoding, digital material generation, edition, and estimation. First, we present a visual attribute transfer framework for digital materials that can effectively generalize to new illumination conditions and geometric distortions. We showcase a use-case of this method for high-resolution material acquisition using a custom device. Additionally, we propose a generative model capable of synthesizing tileable textures from a single input image, which helps improve the quality of material rendering. Building upon recent work in neural fields, we also introduce a material representation that accurately encodes material reflectance while offering powerful editing and propagation capabilities. In addition to reflectance, we present a novel method for global illumination encoding that leverages carefully designed generative models to achieve significantly faster sampling than previous work. Finally, we propose two innovative methods for low-cost material digitization. With flatbed scanners as our capture device, we present a generative model that can provide high-resolution material reflectance estimations using a single image as input, while introducing an uncertainty quantification algorithm that increases its reliability and efficiency. Additionally, we present a novel method for digitizing fabric mechanical properties using depth images as input, which we extend with a perceptually-validated drape similarity metric. Overall, the contributions of this thesis represent significant advances in the fields of radiance encoding and digital material acquisition and edition, enhancing the quality, scalability, and efficiency of physically-based rendering pipelines. 9 Carlos Rodríguez - Pardo Computer Vision News
PlanT: Explainable Planning Transformers via Object-Level Representations Computer Vision News 10 Best Presentation ICVSS PlanT, Katrin’s paper from last year’s Conference on Robot Learning (CoRL), proposed a state-of-the-art learning-based planner for autonomous driving. Autonomous driving can involve a modular pipeline comprising sensor data, a perception module for perceiving the environment around you, and a planner. The planner considers the perception output and the 3D detection of surrounding vehicles and determines the optimal trajectory for the ego vehicle. Katrin Renz is a PhD student at the University of Tübingen in Andreas Geiger’s lab, working on the combination of autonomous driving and language. Last month, she won the Best Presentation prize at Sicily’s International Computer Vision Summer School (ICVSS). She speaks to us about her awardwinning work.
11 Katrin Renz Computer Vision News Check out our Video Interview with Katrin
Computer Vision News 12 Best Presentation ICVSS
13 Computer Vision News Traditionally, planning modules were rule-based, requiring handcrafted rules for every possible scenario. While these methods served their purpose, they lacked flexibility and scalability, making them challenging to adapt to dynamic and complex real-world environments. Previous works perform this learning-based planning using a rendered image and a CNN or a graph or object-level representation evaluated offline. Katrin’s innovative model performs online evaluations of the ego vehicle’s trajectory in real-time simulations, a vital improvement on offline metrics. Other key aspects that set PlanT apart are its simplicity and extendibility. Many existing models use complicated architectures, making implementation and understanding difficult. In contrast, PlanT offers a straightforward yet effective transformer-based approach that can serve as a baseline for various extensions. It tokenizes the scene, making it easily adaptable for other use cases, such as inputting language tokens for combining language and driving. A transformer network inherently has attention weights. Katrin used this feature to enhance the explainability of the model. Attention weights were visualized to determine how much attention the network assigned to each vehicle in the scene when making decisions. Additionally, she proposed an evaluation scheme to assess the reliability of these attention weights to explain the model’s decisions, as she tells us there is some contention in the field around whether attention is explainability. What does Katrin think convinced the jury to award her presentation the top prize? “There was a first, second, and third place, and from what I saw of the others, I think we had the most nonstandard poster,” she reveals. “The posters were not what you see all the time at poster sessions. All three had some kind of fancy layout. A third of my poster was just a red bar with a title, my logo from the project, and the QR code. I had a smaller piece for the content with the really important stuff. It was an eye-catcher. Presentation-wise, many people did a great job conveying what they did and explaining it in a clear way.” Check out our video interview with Katrin to learn more about this work, including the efforts that have already begun to extend it, her thoughts on modular vs end-to-end approaches, and her unconventional path to working in this domain. Katrin Renz
Computer Vision News 14 Women in Computer Vision Watch her in video!
15 Khadija Iddrisu Computer Vision News Khadija Iddrisu is a PhD researcher at Dublin City University and works with the Insight SFI Research Centre for Data Analytics in Ireland. Read 100 FASCINATING interviews with Women in Computer Vision Khadija, you’renot Irish, are you? No, I’mGhanaian. I’mfrom Ghana. How is it to be a scientist from Ghana? This is actually a very intriguing question. In Ghana, there are not a lot of scientists, I would say, in the field of machine learning and AI. It’s just a few people. But for now, I’m part of the Women in Machine Learning, and our agenda is to try to get so many people to pursue research in STEM and AI and related areas. When did you understand that you were going to be a scientist? It started in my undergraduate studies. It was when Covid started, and by then, I was a computer science student, but I didn’t know exactly what I wanted to do after school, so you would find me doing graphic designing, you would find me doing web designing, and it was just like a whole lot of mess! [laughs] When Covid came, and there was a long break, we were not sure when we were going back to school. One time I saw a flyer that said they are having six weeks training in AI. On the flyer, there was a robot, and I like to watch sci-fi movies, so the robot caught my attention. I said that I wanted to know how people build robots, so I would go for this training. Afterwards, I realized it wasn’t just about robots, it was aboutAI. At the end of it, I did a project, and this project was about trying to use computer vision to detect disease in poultry. Even though this project was not a success because by then we didn’t have a lot of datasets and AI was new in Ghana as well, I was just so fascinated by the fact that we can use computer vision to solve problems in almost every field. I just became interested, and I knew that I wanted to pursue further studies in this area, and that is what got me here. How prepared were you for the challenge that awaited you? I’vemet a lot of people on my career path, and they have acted as sort of like career guides, and they are people that I can always go to for advice. Alex [Alessandro Crimi], for instance, he’s a very great person, and he has advised me to work on several projects. With these people, I felt like it would be much easier. Regarding your school path, was it good enough for you to get in contact with complicated technological challenges for a PhD? My undergraduate was very challenging because, by that time, we were not doing a lot of coding, and even if we code, sometimes, during exams, we have to write programs on paper. It was really
Computer Vision News 16 Women in Computer Vision “I just have to keep doing this for the sake of my soul, for the sake of my country, and for the sake of the whole world!” difficult, but afterwards, I had my master’s at the African Institute for Mathematical Sciences, and over there, it was very intensive. We have lecturers from different parts of the world. They would come in and teach us courses for three weeks. In my typical universities in Ghana, we would take three months to study these courses that they are teaching for just three weeks. It was quite an amazing experience. That was actually where I met Alex. He was my advisor for the first teasers that I ever did in machine learning. Going to the African Institute for Mathematical Sciences really gave me a lot of confidence. I gained a lot of skills that I needed to use. All those technical skills that I needed were provided to me there. Afterwards, I also went to the African Masters of Machine Intelligence, which is actually the same as my Master’s in Maths program, but this time, it was sponsored by Google and Facebook. We had lecturers who were industry
17 Computer Vision News workers in Google, we had people who worked at Meta, and they would all come down to Senegal and take us through courses. We would have mock interviews with them as if we are interviewing for roles in their company. We’dalso sometimes have sessions with them on how to apply for PhD programs and how to choose a career path, and I think it really gave me a lot of understanding. That really was a very huge stepping stone to where I am right now. Through all these challenging paths that you just described, how did you keep yourself motivated to overcome these challenges? Yes, that is a very interesting question! [laughs] Mostly, when you’re from Ghana, and from my region specifically – I am from the northern part of Ghana – people believe that girls like myself should only just go to high school and they are done, and they should get married and stuff like that. When I first got to the university, and I realized that I wanted to do something like this, I met about two women that were from the same community as myself, and they went to the same schools as myself, and they were actually doing very well. When I saw that they could actually leave that community and become great people that would be role models to other people, it sort of motivated me and gave me the understanding that I can leave as well, and I can get to motivate many young people as well. That was one thing that was just the driving force for me to go into research. Also, when I realized that with machine learning we could do so many things and Ghana was lagging behind, it was the main driver for me. I realized that if I get a PhD and I have experience, I can always go back to my country and set up a research lab where people would learn about machine learning and AI, and that way, they can also use it to solve problems in their community aswell. What are you currently working on? Khadija Iddrisu
Computer Vision News 18 Women in Computer Vision I applied to a program in machine learning in Dublin, but it was very competitive, and I was put on a waiting list. A few months afterwards, a professor reached out to me that he was impressed by my interview, and he wanted me to work with him on a project. This project is with a company called Xperi. They have offices in Europe and US. As part of the arrangements, I would do my PhD at Dublin City University, and automatically, I am part of the Insight SFI Centre for Data Analytics, but also, as part of my PhD, I would move to their company to work for two years. Their company deals in a lot of computer vision applications for cars, for smart homes, and the current project I am working on is about trying to detect microsleep. Microsleep happens when people are driving, or you are just seated, and then you doze off for five minutes, and then you can’t even believe that you slept. This has led to a lot of accidents. What I am trying to do is to use EEG signals and also just images and videos of people that have been simulated driving and sleeping, and then we try to predict when the microsleep will happen and how long it would occur. That’s where we can implement a system to trigger when someone is going into microsleep. That’s the topic I’m working on for myPhD. We are trying to use a different type of data called data from event cameras. Event cameras are new in the vision system, and the type of data it provides is different from the data we have from traditional cameras. Event cameras give us more data. We can see when a change in brightness occurred, we can see the direction of the brightness change and everything. This is why we are trying to use event cameras, traditional cameras, and EEG signals to predict when a microsleep would occur. I’m still at the early stages of my PhD, so I have been asked to just do some experiments to get used to how event camera data works. For instance, right now, I’mworking on trying to replicate a paper that tries to do eye blink detection. We try to estimate attention level from eye blink detection, and the data is from an event camera. Can you tell us more about those moments when you wanted to give up? How did you handle them? I was telling one of my friends when we were in my master’s project, he would ask me, what is my next step after here? I would tell himI’mnot even thinking about my next steps. I just want to solve this maths question, and I’mhappy
19 Computer Vision News enough. I’ve encountered times when it’s very difficult to keep working on research, especially when you are not getting any new results. I just felt like if I had gone to the industry, if I had just gone to get a regular job, my life would have been easier. But I always refer back to the first research I did with Alex. When I did this research, I had the opportunity to present it at a conference in Tunisia. After I presented this work, a lot of people came up to me asking me questions about how I was able to do this sort of work, how they can apply it to their own research and everything. Then afterwards, I also had the opportunity to present it at a Women in AI conference in Ghana, and people asked me lots of questions about how I was able to do that. How can this be applied to our daily lives? Anytime I’mkind of stuck in a loop where I feel like I don’t want to continue with research anymore, I just refer back to that, and then I realize that this research that I thought I was doing just for fun has actually impacted the lives of so many people. Because of that, I don’t want to stop. I want to keep researching, and I want to keep making the lives of people better. It’s not up to me to decide when I want to stop. I just have to keep doing this for the sake of my soul, for the sake of my country, and for the sake of the whole world, and that’s just what motivates me to keep on going! Khadija Iddrisu
Computer Vision News 20 AI Spotlight News New Chapters of Machine Learning Interviews Book! Our readers already know about the exceptional book by awesome Chip Huyen: Designing Machine Learning Systems. Apparently, O’Reilly is publishing another excellent ML book, this one by Susan Shu Chang: Machine Learning Interviews. We can already read some of the chapters at the following link: ReadMore Computer Vision News has found great new stories, written somewhere else by somebody else. We share them with you, adding a short comment. Enjoy! Training CV to Think Like the Brain Enhances Performance and Robustness This is an article by Cryptopolitan, but don’t worry, it doesn’t talk about crypto currencies. It is about an intriguing work by MIT and IBM aiming at aligning computer vision with human vision. They think this has the potential to advance the field and deepen our understanding of biological neural networks. Science knows which part of the brain is the one humans and monkeys rely on for object recognition. The MIT researchers led by James DiCarlo have made a computer vision model more robust by training it to work like that part. ReadMore How photonics is revolutionizing convolutional neural networks This nice piece by Eurekalert relates how researchers have turned to photonics as a means to enhance convolutional neural networks in a way that consumes less power and requires less memory, which is not mean feat for current voracious CNNs. The study was originally published on Intelligent Computing journal and all the authors of this EU H2020 work come from Greek universities. ReadMore
21 AI Spotlight News Computer Vision News How Do We Know How Smart AI Systems Are? A fantastic article by Melanie Mitchell of the Santa Fe Institute, published onScience about the limitations of LLMs: it’s not (only) me saying that, it’s Yann LeCun, who like Melanie believes that “Taken together, these problems make it hard to conclude - from the evidence given - that AI systems are now or soon will match or exceed human intelligence”. A must read for all AI passionates! ReadMore Glaze, a tool to protect human artists from style mimicry by generative AI models Diffusion models such as MidJourney and Stable Diffusion have been trained on large datasets of scraped images from the web, many of which are copyrighted. They can be then used to copy individual artists, through mimicry. This software called Glaze claims to be able to protect human artists by disrupting style mimicry. The key lays in how AI sees visual information differently from humans. Watch the video Australia Post uses computer vision for site safety Apparently, Australia Post is using computer vision technology as a site safety initiative, to detect team members moving into “unsafe zones” at facilities. The company claims that it made significant investments to protection a workforce of almost 30,000 workers, using machine learning and computer vision technology, supported by Google Cloud Platform (CGP). This is obviously very much needed for interacting with machines such as forklifts, trucks and sorting machines. What a shame that the post did not tell the details of their tech. ReadMore
Computer Vision News 22 AI Bestseller to-be! Fei-Fei Li just announced that her book, The Worlds I See, will be published on Nov 7, at Moment of Lift Books (an imprint from Melinda Gates and Flatiron). She says that “AI can help people and I hope you’ll come along on the journey!” Fei-Fei was General Chair at CVPR 2023 just one month ago and we even had the chance for a cute selfie and a chat about…Computer Vision News!
AUGUST 2023 Learn about Ambient Intelligence for HealthCare on page 38! It’s a MICCAI Workshop
Computer Vision News 24 Congrats, Doctor Leonardo! Leonardo Ayala just gave “one of the best PhD defense presentations” that his supervisor Lena Maier-Hein has ever witnessed. Leo brought much joy during his PhD to the division of Intelligent Medical Systems (IMSY) at the German Cancer Research Center (DKFZ) through the "FUN ministerium" that he established. Congrats, Doctor Leo! When I interviewed for my PhD position, Lena asked me why I wanted to change my research fields from material science to computer science applied to medicine, “Because I would like to do something that has a more direct effect on helping people” I answered. Even though at that moment I was full of doubts, I can now certainly say that I chose the right path, or perhaps it chose me. Spectral imaging (SI) is an imaging technique that, in contrast to traditional RGB (red, green, and blue) imaging, provides much richer Translational Functional Imaging Enabled by Deep Learning
25 Leonardo Ayala Computer Vision News spectral information by collecting light in many narrow regions of the optical spectrum. This property allows SI to encode functional properties (e.g., oxygenation and ischemia monitoring) in diffuse reflectance data. However, its translation to clinical practice is currently hindered by a number of limitations such as image recording speed, controlled illumination restrictions, and high inter-patient data heterogeneity. During my PhD I worked on the development of systems and methods to translate spectral imaging into clinical practice. More precisely, my team and I addressed three main challenges: 1) slow imaging devices, 2) controlled illumination restrictions, and 3) high inter-patient variability and eliminating the need for contrast agents. Among these challenges, the later one was at the core of my dissertation. Emerging imaging modalities such as SI innately face the challenge of limited data availability. Under conditions of data scarcity, high interpatient data variability substantially impedes the development of clinically usable AI models. The challenge arises from the bias introduced in AI models when the distribution of the deployment population differs significantly from the population on which the models were trained. To mitigate this bias, an out-ofdistribution (OoD) detection approach was developed to monitor ischemia during surgery in a personalized manner, which only requires data from one single patient for model training without the need for contrast agents (Fig. 1). More details about this approach can be found in our Science Advances publication. In summary, my work pioneered an entirely novel functional imaging paradigm based on spectral techniques and specifically removed common roadblocks to clinical translation. In doing so, it opens up new avenues of clinical functional imaging to the benefit of patients in surgery and beyond.
Computer Vision News 26 Deep Learning for the Eyes We’re kicking off the Deep Learning for Ophthalmology interview series with multiple-instance learning. A huge thank you goes to José Morano Sánchez, who introduced me to his recently published work Weakly-Supervised Detection of Amd-related Lesions in Color Fundus Images Using Explainable Deep Learning. The goal of the research was to create a pipeline that is able to diagnose age-related macular degeneration (AMD) which is a common cause of irreversible blindness. Instead of using a simple classification black box model for image-level labels of the nine lesion classes in their AMDLesions dataset, weakly supervised learning is applied in order to generate one activation mask for each lesion type to enhance the explainability. Now, how does this connect to multiple-instance learning? As you probably already know, in multipleinstance learning, we have a bag (image) of instances (pixels or groups of pixels). Multiple labels are provided for the entire bag (imageby Christina Bornberg @datascEYEnce Hello, I am Christina! Welcome to the new RSIP Vision column datascEYEnce! I am interested in deep learning applied to ophthalmology! I just finished my master’s in medical image analysis and am now working at the Singapore Eye Research Institute. featuring José Morano Sánchez José is currently a doctoral research scientist in the Christian Doppler Laboratory for Artificial Intelligence in Retina at the Medical University of Vienna. He received his bachelor’s and master’s degrees from the University of A Coruña in Spain, where he also pursued the research on weakly supervised learning which we are focusing on here today. Multiple-instance Learning Inspired Explainable Deep Learning Network
27 datascEYEnce! Computer Vision News level label) instead of each single instance (segmentation mask). The approach makes it possible to generate a single activation map for each one of the nine AMD-related lesion types. Those masks then feed into global max-pooling which has one advantage over Grad-CAM approaches: it provides more intuitive explanation maps. Additionally to the lesion maps, the pipeline produces two more outputs: a vector revealing the presence or absence of a lesion and the final AMD diagnosis. In case you want to use their method for your future work, I collected some additional technical details of their setup worth mentioning: they used a VGG-16 backbone but according to José any backbone could be used since their method is model-agnostic. Here, it is important to exclude the last max-pooling layer from the backbone which would otherwise result in a too small activation map size. The adapted backbone then feeds into a 1x1 convolutional layer with nine output channels, one for each lesion. You can get more information about their workhere. I want to thank José again for the interview and wish him the best of luck for his ongoing PhD journey where he focuses on multimodal and self-supervised learning for retinal imaging! If you are interested in his work and are attending MICCAI 2023, I would recommend to keep an eye out for “Self-supervised learning via inter-modal reconstruction and feature projection networks for labelefficient 3D-to-2D segmentation”! Additionally, you can find a publicly available version here and a code implementation here!
Computer Vision News 28 Best Paper Award at BVM Accurately measuring cell proliferation speed is important for understanding the aggressiveness of tumors. A key element in this assessment is the argyrophilic nucleolar organizer regions (AgNORs) found within cell nuclei, which are correlated with cell proliferation. More AgNORs mean faster proliferation. This paper explores the automatic assessment of AgNORs from histopathology images, paving the way for more precise and informed tumor diagnosis. Alongside other methods, such as Ki-67andcounting mitotic figures, AgNOR-scores offer an additional layer of explainability, shedding light on the pace at which cells divide. Marc Aubreville (left) is a professor at the Technical University Ingolstadt of Applied Sciences in Germany, where Jonathan Ganz (right) is a PhD student, with a co-supervisor at FAU ErlangenNürnberg. They speak to us fresh from winning the Best Paper Award at BVM 2023 last month. Deep Learning-Based Automatic Assessment of AgNOR-scores in Histopathology Images
29 Deep Learning-Based Automatic ... Computer Vision News Exploring AgNORs as a viable assessment tool is no simple task. Jonathan and Marc worked closely with collaborators in veterinary pathology, including Christof Bertram, who was passionate about the topic and instrumental in pushing it forward. The team used supervised learning to establish a substantial canine dataset with roughly 23,000 annotated cells. As AgNORs are primarily counted under light microscopes, limited information exists regarding how accurate humans are at carrying out the task. The researchers conducted a human rater experiment, enlisting pathologists for a comparative study, allowing a unique assessment of the algorithm’s performance against human raters, which resulted in valuable insights into the reliability of both methods.
Computer Vision News 30 This research has established a new task in the domain of machine learning. Many studies focus on well-established tasks with good baselines and data, whereas this work tackles an essential yet unexplored task due to a lack of prior baselines. Also, despite its importance in predicting outcomes, the tedious nature of AgNOR assessment has thus far prevented its integration into routine pathologist workflows. What do Jonathan and Marc think convinced the judges to award the paper the top prize at BVM this year? “Oh, that’s a tough question,” Jonathan remarks. “We did science. We didn’t over-advertise what we did. We know what our algorithm is able to do, and we highlighted our limitations.” Marc adds: “It was just a rock-solid science paper. It was targeted toward insights, not methods. We wanted to find out stuff, see if that’s possible, and how well we do against a human baseline. It was really just the science focus of the paper that the reviewers agreed was actually good. They were happy it wasn’t yet another new method that does yet another 1% increase in something, which is, I think, what many people are a little bit tired of!” Check out our video interview to learn more about this work, including Jonathan andMarc’s ideas for extending it, their ambitions to work on different focal planes, and their regular work around tumor biology. Best Paper Award at BVM Check out our Video Interview with Marc and Jonathan
31 Computer Vision News Best Poster Award at BVM Robert Mendel of the Regensburg Medical Image Computing (ReMIC) group of Ostbayerische Technische Hochschule Regensburg was awarded by the audience the prize for the best poster at the BVM conference. Robert’s poster “Exploring the Effects of Contrastive Learning on Homogeneous Medical Image Data” identified weaknesses of contrastive learning in the medical imaging domain and proposed sampling and masking strategies adapted to the domain's characteristics. Congratulations!
Computer Vision News 32 AI for Surgical Video Analysis We spoke with Asher Patinkin, one of the knowledgeable experts in this field at RSIP Vision. He provided a full review of the most advanced AI and Computer Vision algorithms that can be used for surgical video analysis. Depending on the specific requirements, Deep Learning algorithms, such as convolutional neural networks (CNNs), RNNs can be trained on large datasets of surgical video footage to perform tasks such as object detection, tracking, segmentation, and activity recognition, as follows: Object Detection – Asher points out that Algorithms like YOLO, Faster RCNN, SSD (Single Shot Detector) and RetinaNet help identify specific objects or instruments within surgical video footage, such as surgical tools, implants, or anatomical structures. Tracking – Here we distinguish between traditional algorithms (like Kalman Filter, Mean Shift, Particle Filter) and more recent Deep Learning algorithms, which follow the movement of objects or instruments within the surgical video footage over time, allowing for analysis of the trajectory and motion of these objects. Surgical video analysis involves using artificial intelligence and machine learning algorithms to analyze surgical video footage. This practice, which includes both intraoperative and postoperative video analysis, has numerous benefits for patients, for surgeons and for other medical professionals as well. This is what we use at RSIP Vision.
33 Algorithms for Surgical Video Analysis Computer Vision News Pose Estimation – Pose estimation and Mask R-CNN allow to estimate the position and orientation of instruments or anatomical structures within the surgical field, allowing for analysis of surgical technique and instrument placement. Segmentation – Asher says that UNet, Mask R-CNN, FCN separate objects or instruments within the surgical field from the surrounding environment, allowing for more precise analysis of their movements and interactions. Activity Recognition – It is Asher’s opinion that Transformers and 3D CNN enable to identify and classify specific surgical actions or tasks performed within the surgical field, allowing for analysis of surgical workflow and technique. RSIP Vision’s AI experts and engineers have both the knowledge and the experience to respond to your specific needs in Video Analysis of surgeries and all medical procedures with AI.
Computer Vision News 34 Runner Up Best Poster Award at MIDL Graph neural networks (GNNs) have emerged as a promising approach to enhance the accuracy and efficiency of predicting aneurysm development in the brain. This innovative method represents the brain’s vasculature as a graph, providing a unique understanding of the structure of vessels and potential risks for aneurysms. In recent years, deep neural networks (DNNs) have been shown to be prone to miscalibration, leading to some unreliable predictions. Overconfident probability estimates often cause this miscalibration. By contrast, GNNs tend to be underconfident in their predictions. Previous research has attempted to mitigate the issue of underconfidence in GNNs, but the effectiveness of these techniques remains largely untested in the context of medical image data. This paper aims to address that by determining whether calibration techniques applied to overconfidence in DNNs could be generalized to fix underconfidence in GNNs trained on medical image data. Iris Vos (left) is a fourth-year PhD student at UMC Utrecht in the Netherlands. Her work on the risk prediction of aneurysm development in the brain has just won the Runner Up Best Poster Award at MIDL2023. Calibration Techniques for Node Classification Using Graph Neural Networks on Medical Image Data
35 Calibration Techniques for Node Classification ... Computer Vision News “GNNs are still quite a new topic, and especially for medical image data, it’s difficult to get good graphs because they rely heavily on the segmentations,” Iris tells us. “I focus on the circle of Willis, an anastomosis of blood vessels in the brain. They have a lot of anatomical variety among healthy people as well. The brain vessels form this kind of circle. Only 30% of the people in a healthy population will have this complete circle, and 70% will have some anatomical variance. Blood vessels can be absent, or certain vessels can be duplicated or underdeveloped. To get good graphs, you need good segmentations, especially of the smaller vessels. If they’re underdeveloped, it’s quite difficult.” Iris’s more observational study focuses on the applicability of calibration techniques to graphs and finds that the methods are indeed effective. However, the segmentation challenge is ongoing for researchers. Current and future efforts, including a MICCAI challenge, are focused on segmenting the intracranial arteries and developing more accurate vessel segmentation and graphs moving forward. “We used node classification for GNNs,” Iris explains. “We looked at the most vanilla GNN that there is at the moment, but we also focused on higher-order graph convolutional networks because a limitation in these GNNs is that they focus on local regions. They only learn the embeddings of direct neighboring nodes of a target node. But they fail to capture global patterns in the data. The higher-order graph convolutional networks add information beyond direct neighborhoods, and show better performance in both discriminative power and calibration.” Calibration is a crucial aspect often overlooked in pursuit of high accuracy and discriminative power in classification tasks. It becomes particularly important when the primary goal is not simply distinguishing between two classes, Good calibration means that a model is confident about accurate predictions, while also indicating low confidence when it is likely to be inaccurate. In contrast to most deep neural networks, which are often overconfident in their probability estimates, graph neural networks tend to be underconfident.
Computer Vision News 36 for instance between malignant and non-malignant tumors. In the context of aneurysm development, the objective is to determine subgroups within the population to identify individuals at higher risk. The confidence estimates produced by neural networks play a pivotal role in clinical decision-making. “If you use neural networks, and the produced confidence estimates are over or underconfident, it can lead to real issues in the clinic,” Iris points out. “If we want to identify if a certain person is at risk of developing an aneurysm, and we say it’s 70%, you need to know that your model is not over or underconfident. We want to use these models to decide whether or not we perform follow-up screenings of at-risk individuals!” Although other techniques have already proved to work well, she hopes people will incorporate the essence of this research in their work. It demonstrates how relatively simple it is to calibrate a model or at least report on its uncertainty rather than reporting solely on how a model has obtained a degree of accuracy. Evaluating the uncertainty of a model brings it closer to clinical acceptance. Last year, Iris worked on an interesting project using GNNs for automated intracranial artery labelling, using an atlas to extract atlas-based features that were used as input for node classification. It was named a finalist for an award at the SPIE conference. Runner Up Best Poster Award at MIDL Graph neural networks learn by exchanging information between local neighborhoods of nodes. By adding information on a global scale, using features based on a statistical brain atlas, we were able to improve the performance of node classification
37 Computer Vision News Calibration Techniques for Node Classification ... Does she have any thoughts on what it was about her work this time that convinced the judges to commend it so highly? “I don’t know for sure, but I feel it’s becauseit’s not that mathematically complex or innovative; it’s just an application,” she ponders. “MIDL focuses a lot on reproducibility and scientific impact, and this study is quite easy for other scientists to get some information out of and get some take-home message and then apply some techniques. We also put the code online for the benchmark dataset, so it’s reproducible. But yeah, I was still surprised!” Iris’s supervisor on this work, Hugo Kuijf, should already be familiar to readers of this magazine. She tells us his philosophy is that PhDs should be hard work but also fun. “It felt more like a collaboration than that he was my supervisor,” she recalls. “We got along really well. He taught me a lot about supervising students and how to best motivate a student that’s not doing well. He’s really good in his communication, in his soft skills.” Looking ahead, she is focused on mesh neural networks, a novel approach in medical image data analysis, aiming to identify patterns in vascular structures across individuals to identify different subgroups with varying risks for aneurysm development. Also, combining different fields to obtain the best model – for example, CNNs or meshes with graphs. “In the end, we want to obtain a personalized risk model for each patient,” Iris reveals. “Then, based on that model, we can see if we should perform follow-up screening and how often. This way, we hope to detect aneurysms in an early stage so they can be treated before causing damage.” Iris with the other award winners at MIDL 2023
Computer Vision News 38 MICCAI Workshop Preview Ehsan and Babak speak to us as the co-organizers of an exciting MICCAI 2023 workshop. Ehsan: Based on the MICCAI workshop chairs’ decision, we are going to have a joint workshop with the AICAI workshop. That’s the Ambient Intelligence for Healthcare and Computational Affective Intelligence for Computer-Assisted Interventions workshop. Babak and I were discussing doing a workshop in the medical world that uses more sensor-based type technologies for healthcare applications. Both of us do have a lot of research and publications in this space. After brainstorming, we decided to propose this workshop to MICCAI 2023. The main goal of this workshop is to advance knowledge and technology of intelligent environments for healthcare. The topic is very broad. It’s not limited to any specific sensor modality or any Ehsan Adeli is an Assistant Professor at the Stanford School of Medicine in the Department of Psychiatry and Behavioural Sciences. He is also affiliated with the Computer Science Department and works in the Stanford Vision and Learning Lab. Babak Taati is a senior scientist at the KITE Research Institute, the research arm of the Toronto Rehabilitation Institute, an adult rehabilitation hospital which is part of the University Health Network, a network of research hospitals affiliated with the University of Toronto. He is also an associate professor (status only) in the Department of Computer Science at the University of Toronto, with a cross-appointment in the Institute of Biomedical Engineering, as well as a Vector Institute Faculty Affiliate. Ehsan Adeli Babak Taati