Search Results for author: Hayit Greenspan

Found 28 papers, 1 papers with code

Weakly Supervised Attention Model for RV StrainClassification from volumetric CTPA Scans

no code implementations26 Jul 2021 Noa Cahan, Edith M. Marom, Shelly Soffer, Yiftach Barash, Eli Konen, Eyal Klang, Hayit Greenspan

We developed a weakly supervised deep learning algorithm, with an emphasis on a novel attention mechanism, to automatically classify RV strain on CTPA.

Learning Rotation Invariant Features for Cryogenic Electron Microscopy Image Reconstruction

no code implementations10 Jan 2021 Koby Bibas, Gili Weiss-Dicker, Dana Cohen, Noa Cahan, Hayit Greenspan

A fundamental step in the recovering of the 3D single-particle structure is to align its 2D projections; thus, the construction of a canonical representation with a fixed rotation angle is required.

Image Reconstruction Rotated MNIST

COVID-19 in CXR: from Detection and Severity Scoring to Patient Disease Monitoring

no code implementations4 Aug 2020 Rula Amer, Maayan Frid-Adar, Ophir Gozes, Jannette Nassar, Hayit Greenspan

In this work, we estimate the severity of pneumonia in COVID-19 patients and conduct a longitudinal study of disease progression.

A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises

no code implementations2 Aug 2020 S. Kevin Zhou, Hayit Greenspan, Christos Davatzikos, James S. Duncan, Bram van Ginneken, Anant Madabhushi, Jerry L. Prince, Daniel Rueckert, Ronald M. Summers

In this survey paper, we first present traits of medical imaging, highlight both clinical needs and technical challenges in medical imaging, and describe how emerging trends in deep learning are addressing these issues.

Nodule2vec: a 3D Deep Learning System for Pulmonary Nodule Retrieval Using Semantic Representation

no code implementations11 Jul 2020 Ilia Kravets, Tal Heletz, Hayit Greenspan

Content-based retrieval supports a radiologist decision making process by presenting the doctor the most similar cases from the database containing both historical diagnosis and further disease development history.

Content-Based Image Retrieval Decision Making +2

Semi-supervised lung nodule retrieval

no code implementations4 May 2020 Mark Loyman, Hayit Greenspan

However, in a previous study, we have shown that binary auxiliary tasks are inferior to the usage of a rough similarity estimate that are derived from data annotations.

Content-Based Image Retrieval Decision Making +2

Joint Liver Lesion Segmentation and Classification via Transfer Learning

no code implementations MIDL 2019 Michal Heker, Hayit Greenspan

In this work, we study the combination of these two approaches for the problem of liver lesion segmentation and classification.

Classification General Classification +3

Coronavirus Detection and Analysis on Chest CT with Deep Learning

no code implementations6 Apr 2020 Ophir Gozes, Maayan Frid-Adar, Nimrod Sagie, Huangqi Zhang, Wenbin Ji, Hayit Greenspan

The outbreak of the novel coronavirus, officially declared a global pandemic, has a severe impact on our daily lives.

Bone Structures Extraction and Enhancement in Chest Radiographs via CNN Trained on Synthetic Data

no code implementations20 Mar 2020 Ophir Gozes, Hayit Greenspan

Using HU based segmentation of bone structures in the CT domain, a synthetic 2D "Bone x-ray" DRR is produced and used for training the network.

Rapid AI Development Cycle for the Coronavirus (COVID-19) Pandemic: Initial Results for Automated Detection & Patient Monitoring using Deep Learning CT Image Analysis

no code implementations10 Mar 2020 Ophir Gozes, Maayan Frid-Adar, Hayit Greenspan, Patrick D. Browning, Huangqi Zhang, Wenbin Ji, Adam Bernheim, Eliot Siegel

We conducted multiple retrospective experiments to analyze the performance of the system in the detection of suspected COVID-19 thoracic CT features and to evaluate evolution of the disease in each patient over time using a 3D volume review, generating a Corona score.

COVID-19 Image Segmentation

A Soft STAPLE Algorithm Combined with Anatomical Knowledge

no code implementations26 Oct 2019 Eytan Kats, Jacob Goldberger, Hayit Greenspan

Supervised machine learning algorithms, especially in the medical domain, are affected by considerable ambiguity in expert markings.

Automatic Segmentation of Muscle Tissue and Inter-muscular Fat in Thigh and Calf MRI Images

no code implementations1 Oct 2019 Rula Amer, Jannette Nassar, David Bendahan, Hayit Greenspan, Noam Ben-Eliezer

Magnetic resonance imaging (MRI) of thigh and calf muscles is one of the most effective techniques for estimating fat infiltration into muscular dystrophies.

Endotracheal Tube Detection and Segmentation in Chest Radiographs using Synthetic Data

no code implementations20 Aug 2019 Maayan Frid-Adar, Rula Amer, Hayit Greenspan

Chest radiographs are frequently used to verify the correct intubation of patients in the emergency room.

Deep Feature Learning from a Hospital-Scale Chest X-ray Dataset with Application to TB Detection on a Small-Scale Dataset

no code implementations3 Jun 2019 Ophir Gozes, Hayit Greenspan

The recent emergence of a large Chest X-ray dataset opened the possibility for learning features that are specific to the X-ray analysis task.

Transfer Learning

Soft labeling by Distilling Anatomical knowledge for Improved MS Lesion Segmentation

no code implementations26 Jan 2019 Eytan Kats, Jacob Goldberger, Hayit Greenspan

Detection and segmentation of MS lesions is a complex task largely due to the extreme unbalanced data, with very small number of lesion pixels that can be used for training.

Lesion Segmentation

Improving CNN Training using Disentanglement for Liver Lesion Classification in CT

no code implementations1 Nov 2018 Avi Ben-Cohen, Roey Mechrez, Noa Yedidia, Hayit Greenspan

Training data is the key component in designing algorithms for medical image analysis and in many cases it is the main bottleneck in achieving good results.

Disentanglement General Classification +2

An Adversarial Learning Approach to Medical Image Synthesis for Lesion Detection

no code implementations25 Oct 2018 Liyan Sun, Jiexiang Wang, Yue Huang, Xinghao Ding, Hayit Greenspan, John Paisley

Being able to provide a "normal" counterpart to a medical image can provide useful side information for medical imaging tasks like lesion segmentation or classification validated by our experiments.

Data Augmentation General Classification +4

Lung Structures Enhancement in Chest Radiographs via CT based FCNN Training

no code implementations14 Oct 2018 Ophir Gozes, Hayit Greenspan

Two 2D FCNN architectures were trained to accomplish the task: The first performs 2D lung segmentation which is used for normalization of the lung area.

A Mixture of Views Network with Applications to the Classification of Breast Microcalcifications

no code implementations19 Mar 2018 Yaniv Shachor, Hayit Greenspan, Jacob Goldberger

We present a decision concept which explicitly takes into account the input multi-view structure, where for each case there is a different subset of relevant views.

General Classification

Cross-Modality Synthesis from CT to PET using FCN and GAN Networks for Improved Automated Lesion Detection

no code implementations21 Feb 2018 Avi Ben-Cohen, Eyal Klang, Stephen P. Raskin, Shelly Soffer, Simona Ben-Haim, Eli Konen, Michal Marianne Amitai, Hayit Greenspan

Quantitative evaluation was conducted using an existing lesion detection software, combining the synthesized PET as a false positive reduction layer for the detection of malignant lesions in the liver.

Lesion Detection

Synthetic Data Augmentation using GAN for Improved Liver Lesion Classification

no code implementations8 Jan 2018 Maayan Frid-Adar, Eyal Klang, Michal Amitai, Jacob Goldberger, Hayit Greenspan

In this paper, we present a data augmentation method that generates synthetic medical images using Generative Adversarial Networks (GANs).

Computed Tomography (CT) Data Augmentation +2

Anatomical Data Augmentation For CNN based Pixel-wise Classification

no code implementations7 Jan 2018 Avi Ben-Cohen, Eyal Klang, Michal Marianne Amitai, Jacob Goldberger, Hayit Greenspan

In this work we propose a method for anatomical data augmentation that is based on using slices of computed tomography (CT) examinations that are adjacent to labeled slices as another resource of labeled data for training the network.

Classification Computed Tomography (CT) +2

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