2k citations · 7k across the 39 of their papers we have counts for
40 papers
SymmAdapt: Symmetrical Flow Matching for Source-Free Domain Adaptation in Medical Image Segmentation
Tal Grossman, Noa Cahan, Hayit Greenspan
Domain shift across imaging modalities and acquisition sites remains a significant barrier to the clinical deployment of segmentation models. Source-free unsupervised domain adapta…
DiffuSAM: Diffusion-Based Prompt-Free SAM2 for Few-Shot and Source-Free Medical Image Segmentation
Tal Grossman, Noa Cahan, Lev Ayzenberg +1
Segmentation models such as Segment Anything Model (SAM) and SAM2 achieve strong prompt-driven zero-shot performance. However, their training on natural images limits domain transf…
ProtoSAM: One-Shot Medical Image Segmentation With Foundational Models
Lev Ayzenberg, Raja Giryes, Hayit Greenspan
This work introduces a new framework, ProtoSAM, for one-shot medical image segmentation. It combines the use of prototypical networks, known for few-shot segmentation, with SAM - a…
X-ray2CTPA: Leveraging Diffusion Models to Enhance Pulmonary Embolism Classification
Noa Cahan, Eyal Klang, Galit Aviram +4
Chest X-rays or chest radiography (CXR), commonly used for medical diagnostics, typically enables limited imaging compared to computed tomography (CT) scans, which offer more detai…
DINOv2 based Self Supervised Learning For Few Shot Medical Image Segmentation
Lev Ayzenberg, Raja Giryes, Hayit Greenspan
Deep learning models have emerged as the cornerstone of medical image segmentation, but their efficacy hinges on the availability of extensive manually labeled datasets and their a…
A Self Supervised StyleGAN for Image Annotation and Classification with Extremely Limited Labels
Dana Cohen Hochberg, Hayit Greenspan, Raja Giryes
The recent success of learning-based algorithms can be greatly attributed to the immense amount of annotated data used for training. Yet, many datasets lack annotations due to the…
Neural Network Reconstruction of the Left Atrium using Sparse Catheter Paths
Alon Baram, Moshe Safran, Tomer Noy +2
Catheter based radiofrequency ablation for pulmonary vein isolation has become the first line of treatment for atrial fibrillation in recent years. This requires a rather accurate…
HeartBEiT: Vision Transformer for Electrocardiogram Data Improves Diagnostic Performance at Low Sample Sizes
Akhil Vaid, Joy Jiang, Ashwin Sawant +9
The electrocardiogram (ECG) is a ubiquitous diagnostic modality. Convolutional neural networks (CNNs) applied towards ECG analysis require large sample sizes, and transfer learning…
Weakly Supervised Attention Model for RV StrainClassification from volumetric CTPA Scans
Noa Cahan, Edith M. Marom, Shelly Soffer +4
Pulmonary embolus (PE) refers to obstruction of pulmonary arteries by blood clots. PE accounts for approximately 100,000 deaths per year in the United States alone. The clinical pr…
Learning Rotation Invariant Features for Cryogenic Electron Microscopy Image Reconstruction
Koby Bibas, Gili Weiss-Dicker, Dana Cohen +2
Cryo-Electron Microscopy (Cryo-EM) is a Nobel prize-winning technology for determining the 3D structure of particles at near-atomic resolution. A fundamental step in the recovering…
Automated triage of COVID-19 from various lung abnormalities using chest CT features
Dor Amran, Maayan Frid-Adar, Nimrod Sagie +3
The outbreak of COVID-19 has lead to a global effort to decelerate the pandemic spread. For this purpose chest computed-tomography (CT) based screening and diagnosis of COVID-19 su…
A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises
S. Kevin Zhou, Hayit Greenspan, Christos Davatzikos +6
Since its renaissance, deep learning has been widely used in various medical imaging tasks and has achieved remarkable success in many medical imaging applications, thereby propell…
COVID-19 in CXR: from Detection and Severity Scoring to Patient Disease Monitoring
Rula Amer, Maayan Frid-Adar, Ophir Gozes +2
In this work, we estimate the severity of pneumonia in COVID-19 patients and conduct a longitudinal study of disease progression. To achieve this goal, we developed a deep learning…
Nodule2vec: a 3D Deep Learning System for Pulmonary Nodule Retrieval Using Semantic Representation
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 fu…
Joint Liver Lesion Segmentation and Classification via Transfer Learning
Michal Heker, Hayit Greenspan
Transfer learning and joint learning approaches are extensively used to improve the performance of Convolutional Neural Networks (CNNs). In medical imaging applications in which th…
Semi-supervised lung nodule retrieval
Mark Loyman, Hayit Greenspan
Content based image retrieval (CBIR) provides the clinician with visual information that can support, and hopefully improve, his or her decision making process. Given an input quer…
Coronavirus Detection and Analysis on Chest CT with Deep Learning
Ophir Gozes, Maayan Frid-Adar, Nimrod Sagie +3
The outbreak of the novel coronavirus, officially declared a global pandemic, has a severe impact on our daily lives. As of this writing there are approximately 197,188 confirmed c…
Rapid AI Development Cycle for the Coronavirus (COVID-19) Pandemic: Initial Results for Automated Detection & Patient Monitoring using Deep Learning CT Image Analysis
Ophir Gozes, Maayan Frid-Adar, Hayit Greenspan +5
Purpose: Develop AI-based automated CT image analysis tools for detection, quantification, and tracking of Coronavirus; demonstrate they can differentiate coronavirus patients from…
Bone Structures Extraction and Enhancement in Chest Radiographs via CNN Trained on Synthetic Data
Ophir Gozes, Hayit Greenspan
In this paper, we present a deep learning-based image processing technique for extraction of bone structures in chest radiographs using a U-Net FCNN. The U-Net was trained to accom…
A Soft STAPLE Algorithm Combined with Anatomical Knowledge
Eytan Kats, Jacob Goldberger, Hayit Greenspan
Supervised machine learning algorithms, especially in the medical domain, are affected by considerable ambiguity in expert markings. In this study we address the case where the exp…
Automatic Segmentation of Muscle Tissue and Inter-muscular Fat in Thigh and Calf MRI Images
Rula Amer, Jannette Nassar, David Bendahan +2
Magnetic resonance imaging (MRI) of thigh and calf muscles is one of the most effective techniques for estimating fat infiltration into muscular dystrophies. The infiltration of ad…
Endotracheal Tube Detection and Segmentation in Chest Radiographs using Synthetic Data
Maayan Frid-Adar, Rula Amer, Hayit Greenspan
Chest radiographs are frequently used to verify the correct intubation of patients in the emergency room. Fast and accurate identification and localization of the endotracheal (ET)…
Hierarchical Fine-Tuning for joint Liver Lesion Segmentation and Lesion Classification in CT
Michal Heker, Avi Ben-Cohen, Hayit Greenspan
We present an automatic method for joint liver lesion segmentation and classification using a hierarchical fine-tuning framework. Our dataset is small, containing 332 2-D CT examin…
Deep Feature Learning from a Hospital-Scale Chest X-ray Dataset with Application to TB Detection on a Small-Scale Dataset
Ophir Gozes, Hayit Greenspan
The use of ImageNet pre-trained networks is becoming widespread in the medical imaging community. It enables training on small datasets, commonly available in medical imaging tasks…