1.2k citations · 1.4k across the 10 of their papers we have counts for
10 papers
Multi-label Classification with Partial Annotations using Class-aware Selective Loss
Emanuel Ben-Baruch, Tal Ridnik, Itamar Friedman +4
Large-scale multi-label classification datasets are commonly, and perhaps inevitably, partially annotated. That is, only a small subset of labels are annotated per sample. Differen…
Semantic Diversity Learning for Zero-Shot Multi-label Classification
Avi Ben-Cohen, Nadav Zamir, Emanuel Ben Baruch +2
Training a neural network model for recognizing multiple labels associated with an image, including identifying unseen labels, is challenging, especially for images that portray nu…
Compact Network Training for Person ReID
Hussam Lawen, Avi Ben-Cohen, Matan Protter +2
The task of person re-identification (ReID) has attracted growing attention in recent years leading to improved performance, albeit with little focus on real-world applications. Mo…
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…
The Liver Tumor Segmentation Benchmark (LiTS)
Patrick Bilic, Patrick Christ, Hongwei Bran Li +106
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedi…
Improving CNN Training using Disentanglement for Liver Lesion Classification in CT
Avi Ben-Cohen, Roey Mechrez, Noa Yedidia +1
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. Recent progress in imag…
Improving the Segmentation of Anatomical Structures in Chest Radiographs using U-Net with an ImageNet Pre-trained Encoder
Maayan Frid-Adar, Avi Ben-Cohen, Rula Amer +1
Accurate segmentation of anatomical structures in chest radiographs is essential for many computer-aided diagnosis tasks. In this paper we investigate the latest fully-convolutiona…
Cross-Modality Synthesis from CT to PET using FCN and GAN Networks for Improved Automated Lesion Detection
Avi Ben-Cohen, Eyal Klang, Stephen P. Raskin +5
In this work we present a novel system for generation of virtual PET images using CT scans. We combine a fully convolutional network (FCN) with a conditional generative adversarial…
Anatomical Data Augmentation For CNN based Pixel-wise Classification
Avi Ben-Cohen, Eyal Klang, Michal Marianne Amitai +2
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 anot…
Virtual PET Images from CT Data Using Deep Convolutional Networks: Initial Results
Avi Ben-Cohen, Eyal Klang, Stephen P. Raskin +2
In this work we present a novel system for PET estimation using CT scans. We explore the use of fully convolutional networks (FCN) and conditional generative adversarial networks (…