Özgün Çiçek

TL;DR: In this paper, the authors propose a network for volumetric segmentation that learns from sparsely annotated volumetrized images, which is trained end-to-end from scratch, i.e., no pre-trained network is required.

TL;DR: An ImageJ plugin is presented that enables non-machine-learning experts to analyze their data with U-Net on either a local computer or a remote server/cloud service.

TL;DR: The proposed network extends the previous u-net architecture from Ronneberger et al. by replacing all 2D operations with their 3D counterparts and performs on-the-fly elastic deformations for efficient data augmentation during training.

TL;DR: A transcriptional atlas of myeloid subsets in experimental autoimmune encephalomyelitis (EAE), a mouse model of MS, shows that dendritic cells and monocyte-derived cells, but not resident macrophages, played a critical role by presenting antigen to pathogenic T cells, which may inform future therapeutic targeting strategies in MS.

TL;DR: In this article, the authors compare several strategies and techniques to estimate uncertainty in a large-scale computer vision task like optical flow estimation, and introduce a new network architecture and loss function that enforce complementary hypotheses and provide uncertainty estimates efficiently with a single forward pass and without the need for sampling or ensembles.