Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Centre National de la Recherche ScientifiqueFR550 papers
- European Organization for Nuclear ResearchCH528 papers
- University College LondonGB520 papers
- University of CambridgeGB423 papers
- University of OxfordGB419 papers
- The University of TokyoJP352 papers
- Imperial College LondonGB340 papers
- Sorbonne UniversitéFR332 papers
- University of SheffieldGB328 papers
- Université Paris-SaclayFR326 papers
- Sapienza University of RomeIT321 papers
- University of GlasgowGB318 papers
10 papers · 2 filters
Explaining Away Results in Accurate and Tolerant Template Matching
M. W. Spratling
Recognising and locating image patches or sets of image features is an important task underlying much work in computer vision. Traditionally this has been accomplished using templa…
Privacy-preserving Federated Brain Tumour Segmentation
Wenqi Li, Fausto Milletarì, Daguang Xu +8
Due to medical data privacy regulations, it is often infeasible to collect and share patient data in a centralised data lake. This poses challenges for training machine learning al…
A Topological Loss Function for Deep-Learning based Image Segmentation using Persistent Homology
James R. Clough, Nicholas Byrne, Ilkay Oksuz +3
We introduce a method for training neural networks to perform image or volume segmentation in which prior knowledge about the topology of the segmented object can be explicitly pro…
Permutohedral Attention Module for Efficient Non-Local Neural Networks
Samuel Joutard, Reuben Dorent, Amanda Isaac +3
Medical image processing tasks such as segmentation often require capturing non-local information. As organs, bones, and tissues share common characteristics such as intensity, sha…
Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge
Hugo J. Kuijf, J. Matthijs Biesbroek, Jeroen de Bresser +41
Quantification of cerebral white matter hyperintensities (WMH) of presumed vascular origin is of key importance in many neurological research studies. Currently, measurements are o…
Training recurrent neural networks robust to incomplete data: application to Alzheimer's disease progression modeling
Mostafa Mehdipour Ghazi, Mads Nielsen, Akshay Pai +4
Disease progression modeling (DPM) using longitudinal data is a challenging machine learning task. Existing DPM algorithms neglect temporal dependencies among measurements, make pa…
Large-scale mammography CAD with Deformable Conv-Nets
Stephen Morrell, Zbigniew Wojna, Can Son Khoo +2
State-of-the-art deep learning methods for image processing are evolving into increasingly complex meta-architectures with a growing number of modules. Among them, region-based ful…
Adversarial training with cycle consistency for unsupervised super-resolution in endomicroscopy
Daniele Ravì, Agnieszka Barbara Szczotka, Stephen P Pereira +1
In recent years, endomicroscopy has become increasingly used for diagnostic purposes and interventional guidance. It can provide intraoperative aids for real-time tissue characteri…
Explicit topological priors for deep-learning based image segmentation using persistent homology
James R. Clough, Ilkay Oksuz, Nicholas Byrne +2
We present a novel method to explicitly incorporate topological prior knowledge into deep learning based segmentation, which is, to our knowledge, the first work to do so. Our meth…
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…