1.3k citations · 3.1k across the 9 of their papers we have counts for
10 papers
Inferring a Continuous Distribution of Atom Coordinates from Cryo-EM Images using VAEs
Dan Rosenbaum, Marta Garnelo, Michal Zielinski +10
Cryo-electron microscopy (cryo-EM) has revolutionized experimental protein structure determination. Despite advances in high resolution reconstruction, a majority of cryo-EM experi…
The Medical Segmentation Decathlon
Michela Antonelli, Annika Reinke, Spyridon Bakas +55
International challenges have become the de facto standard for comparative assessment of image analysis algorithms given a specific task. Segmentation is so far the most widely inv…
Contrastive Training for Improved Out-of-Distribution Detection
Jim Winkens, Rudy Bunel, Abhijit Guha Roy +10
Reliable detection of out-of-distribution (OOD) inputs is increasingly understood to be a precondition for deployment of machine learning systems. This paper proposes and investiga…
A Hierarchical Probabilistic U-Net for Modeling Multi-Scale Ambiguities
Simon A. A. Kohl, Bernardino Romera-Paredes, Klaus H. Maier-Hein +5
Medical imaging only indirectly measures the molecular identity of the tissue within each voxel, which often produces only ambiguous image evidence for target measures of interest,…
A large annotated medical image dataset for the development and evaluation of segmentation algorithms
Amber L. Simpson, Michela Antonelli, Spyridon Bakas +21
Semantic segmentation of medical images aims to associate a pixel with a label in a medical image without human initialization. The success of semantic segmentation algorithms is c…
Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy
Stanislav Nikolov, Sam Blackwell, Alexei Zverovitch +26
Over half a million individuals are diagnosed with head and neck cancer each year worldwide. Radiotherapy is an important curative treatment for this disease, but it requires manua…
A Probabilistic U-Net for Segmentation of Ambiguous Images
Simon A. A. Kohl, Bernardino Romera-Paredes, Clemens Meyer +6
Many real-world vision problems suffer from inherent ambiguities. In clinical applications for example, it might not be clear from a CT scan alone which particular region is cancer…
3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
Özgün Çiçek, Ahmed Abdulkadir, Soeren S. Lienkamp +2
This paper introduces a network for volumetric segmentation that learns from sparsely annotated volumetric images. We outline two attractive use cases of this method: (1) In a semi…
Gland Segmentation in Colon Histology Images: The GlaS Challenge Contest
Korsuk Sirinukunwattana, Josien P. W. Pluim, Hao Chen +16
Colorectal adenocarcinoma originating in intestinal glandular structures is the most common form of colon cancer. In clinical practice, the morphology of intestinal glands, includi…
U-Net: Convolutional Networks for Biomedical Image Segmentation
Olaf Ronneberger, Philipp Fischer, Thomas Brox
There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that r…