Chest CT Scan Analysis with Deep Learning
Chest radiography, with modalities such as X-Ray and CT, is now the common practice for the detection and analysis of the progression of lung tumors, tuberculosis and other pulmonary abnormalities. To date, most analysis are done by expert radiographers, who analyze resulting scans and estimate p...
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Object Tracking at High fps
Object tracking in video sequences is a classical challenge in computer vision, which finds applications in nearly all domains of the industry: from assembly line automation, security, traffic control, automatic driving assistance systems and agriculture. Presently state of the art algorithms per...
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High Resolution Image Reconstruction
Recovering a high-resolution (HR) image from a low resolution one is a classical problem in computer vision for which many algorithms have been developed to date. Most notably, methodologies using sparse coding: these techniques have achieved current state-of-the-art results, but suffer from long...
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Automated Defect Inspection Using Deep Learning
Defect detection during production is a necessary step to ensure product quality. Although manual human inspections are still being employed, automated visual inspection has practically replaced manual labor in almost all major production lines and is ubiquitous in mechanical parts manufacturing,...
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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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Pattern Matching Algorithms
Pattern matching in computer vision refers to a set of computational techniques which enable the localization of a template pattern in a sample image or signal. Such template pattern can be a specific facial feature, an object of known characteristics or a speech pattern such as a word. Many of t...
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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 dry eye disease. The density of ...
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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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OCR for robots
OCR (Optical Character Recognition) modules are being used in robotics. They allow robots to understand text, an essential function in some robotic applications. OCR capability enables robots to handle items identified by imprinted text. Retail applications use robots to identify products on shel...
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Machine Vision Robots for Semiconductors
Robots have been used in the semiconductor industry for a long time. The functions of robots for semiconductors manufacturing operations are spawn over a wide range of tasks, from mechanical tasks up to intelligent tasks. The latter use machine vision. Defect detection in semiconductors manufactu...
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Robots using Machine Vision in Agriculture
Robots are widely used today in agriculture tasks. Many of these tasks require machine vision algorithms to operate successfully. The robots and their machine vision algorithms change form to best suit their function, starting from fields plowing, seeds planting, weeds handling, growth monitoring...
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Algorithms for Autonomous Driving
The transportation revolution coming with autonomous cars has already started, involving challenges that require proper handling of advanced technologies and algorithms: deep learning, to name one. . . Autonomous Driving is classified according to 5 Levels, all higher than Level Zero, where no au...
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