2k citations · 3.3k across the 7 of their papers we have counts for
7 papers
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…
GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification
Maayan Frid-Adar, Idit Diamant, Eyal Klang +3
Deep learning methods, and in particular convolutional neural networks (CNNs), have led to an enormous breakthrough in a wide range of computer vision tasks, primarily by using lar…
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…
Synthetic Data Augmentation using GAN for Improved Liver Lesion Classification
Maayan Frid-Adar, Eyal Klang, Michal Amitai +2
In this paper, we present a data augmentation method that generates synthetic medical images using Generative Adversarial Networks (GANs). We propose a training scheme that first u…
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 (…