Deep Learning in Cardiology
1.1 Segmentation tasks [10] suggest a new fully convolutional network architecture for the task of cardiovascular MRI segmentation. The architecture is based on the idea of network blocks in which each layer is densely connected with auxiliary side paths (skip connections) to all the following la...
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Deep Learning in Pulmonology
Deep learning has been successfully applied in various applications in pulmonary imaging, including CT registration, airway mapping, real time catheter navigation, and pulmonary nodule detection. Some of these applications are still in ongoing development, and here we review few of the most recen...
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Deep Learning in Ophthalmology
Recent works suggest novel deep learning tools for detection, segmentation and characterization of eye disorders. Accurate segmentation of retinal fundus lesions and anomalies in imaging data is an important technical step for early detection and treatment of common eye disorders, and a central a...
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Deep Learning in Brain Imaging
In this article we discuss several recent leading works about Deep Learning in brain imaging and brain microscopy. We organize the works in subsections according to the general algorithmic tasks: segmentation, registration, classification, image enhancement or other tasks. The categories are not ...
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Deep Learning in Medical Imaging
Medical imaging and medical image data analysis are rapidly growing fields. The increasing amounts of available data due to advances and ubiquity of imaging technologies give rise to new medical applications and to new requirements in existing applications, and lead to an increasing demand for ne...
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Wafer Macro Defects Detection and Classification
Defect detection is an integral part of wafer (chip) fabrication process. It enables defect detection and classification along the process to increase the fab yield (amount of good chips out of total wafers processed). Every detected defect is handled as an indicator of some process malfunction. ...
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Echo Cancellation Using Deep Learning
Complete cancellation of returned acoustic echo signal is still an unresolved issue in signal processing. When a signal from a speaker in one end of a room returns and is fed into a microphone, a delayed and distorted version of the input signal is registered and transferred to the transmitting e...
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Classification and Segmentation of Dendritic Cells
Dry eye disease (DED) is one of the most common ophthalmic disorders. Inflammation of the ocular surface is controlled by corneal antigen-presenting cells called dendritic cells (DCs), which induce T-cell activation, and play a critical role in the pathogenesis of DED. The density of corneal DC i...
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Fingerprint Segmentation Using Deep Learning
Automatic fingerprint recognition systems are based on the extraction of features from scanned fingerprint image. A successful preprocessing of the scan is an important first step towards a successful recognition, that is, comparison against a known database or the extraction of information chara...
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Intracranial Hemorrhage and Edema Segmentation
An intracranial hemorrhage (ICH) is a condition in which a blood vessel erupts inside the brain, causing internal bleeding. If not treated correctly and immediately, a brain hemorrhage can be deadly. The type of hemorrhage is usually diagnosed using a CT or MRI scan. Some hemorrhages are also acc...
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3D Cardiac MRI automatic segmentation
Massive progress in 3D Cardiac magnetic resonance imaging (MRI) gives today access to good 3D mapping of the heart, using gating to synchronize to the systolic phase of the pulse. MRI can give good distinction between the different tissues and blood without any hazard connected to radiation. 3D C...
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Object Detection Methods for Robots
Object detection and classification are major challenges for robotic modules. Navigation, Pick and Place and additional robotics activities are based on the ability to recognize object. Recent years has provided a great progress in object detection mainly due to machine learning methods that beca...
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Automatic semantic tagging of images
Image and object recommender systems have been developed along with the Internet itself. The recommender systems are constructed to assist user’s navigation through the variety of content and products (videos, images or objects sold on a website) by correlating user preferences with the item’s ch...
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Detecting Mitosis Using Deep Neural Networks
Several prognostic factors indicate the state and progression of breast cancer. Grading of the state of progression and spreading of cancer enables to start the suggested treatment. In solid tumors, like breast cancer, one such prognostic factor is the mitotic figure, i.e. a cell whose chromosome...
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Vessel Segmentation Using Deep Learning
Using Deep Neural Networks for Vessel Segmentation in Fundus Images Vessel segmentation methods based on image processing techniques have long been utilized to delineate the vascular tree in clinical imaging. Many vessel segmentation algorithms exist and have been widely demonstrated in o...
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Finding Cysts, Part Five: Final Detection
Cyst Detection Using Convolutional Neural Networks Graph-search We have been working on automatically detecting the appearance of Cystoid Macular Edema (CME) in Optical Coherence Tomography (OCT) images. CME is a buildup of fluids in the eye near the macula of the eye that can distort a person&#...
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Automatic Detection of Macular Cysts
Hacking the Automatic Detection Process: Finding Eye Cysts with A.I. Introduction Cystoid Macular Edema (CME) is a buildup of fluids in the eye near the macula of the eye that can distort a person’s vision. Optical coherence tomography (OCT) is one of the main methods by which physic...
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Defining the Borders within Computer Vision
What’s the Difference between Computer Vision, Image Processing and Machine Learning? Computer vision, image processing, signal processing, machine learning – you’ve heard the terms but what’s the difference between them? Each of these fields is based on the input o...
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Reflections on CVPR 2015
Reflections on CVPR 2015 One of our colleagues, Dr. Micha Feigin, presents his thoughts on this year’s Computer Vision & Pattern Recognition Conference: Coming back from CVPR 2015, the main conclusion is that everyone (including us) is doing neural networks, preferably deep learning. S...
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Exploring Deep Learning & CNNs
Deep Learning and Convolutional Neural Networks: RSIP Vision Blogs From robots to drug design, it’s hard to miss “deep learning” in the news and in our office lately. Indeed, RSIP Vision is utilizing deep learning and convolutional neural networks in our classification work. What is deep l...
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