Eyal Klang

TL;DR: It is shown that generated medical images can be used for synthetic data augmentation, and improve the performance of CNN for medical image classification, and generalize to other medical classification applications and thus support radiologists’ efforts to improve diagnosis.

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: SLE is so far the autoimmune disease with the largest number of detectable autoantibodies, and their production could be antigen-driven, the result of a polyclonal B cell activation, impaired apoptotic pathways, or the outcome of an idiotypic network dysregulation.

TL;DR: Deep learning technology provides accurate and fast automated detection of mucosal ulcers on CE images and individual patient-level analysis provided high and consistent diagnostic accuracy with shortened reading time; in the future deep learning algorithms may augment and facilitate CE reading.