Avi Ben-Cohen

TL;DR: The set-up and results of the Liver Tumor Segmentation Benchmark (LITS) organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2016 and International Conference on Medical Image Computing Computer Assisted Intervention (MICCAI) 2017 are reported.

TL;DR: An introduction to deep learning technology is provided and the stages that are entailed in the design process of deep learning radiology research are presented and the results of a survey of the application of convolutional neural networks to radiologic imaging are detailed.

TL;DR: This work explores a fully convolutional network (FCN) for the task of liver segmentation and liver metastases detection in computed tomography (CT) examinations and results indicate superiority of the FCN over all other methods tested.

TL;DR: A fully convolutional network (FCN) with a conditional generative adversarial network (GAN) to generate simulated PET data from given input CT data to enable lesion detection and drug treatment evaluation in a CT-only environment, thus reducing the need for the more expensive and radioactive PET/CT scan.

TL;DR: In this article, a conditional generative adversarial network (GAN) was used to generate virtual PET images from CT scans for false positive reduction in lesion detection solutions, which showed a 28% reduction in the average false positive rate from 2.9 to 2.1.