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- Universidade de BrasíliaBR10 papers
- Guangzhou UniversityCN9 papers
- Lincoln University - PennsylvaniaUS9 papers
- École Polytechnique Fédérale de LausanneCH7 papers
- Sobolev Institute of MathematicsRU7 papers
- University of CambridgeGB7 papers
- University of OxfordGB7 papers
- Centre National de la Recherche ScientifiqueFR6 papers
- Technische Universität DarmstadtDE6 papers
- The University of TokyoJP6 papers
- University of GlasgowGB6 papers
- University of LeedsGB6 papers
5 papers · 1 filter
NuSegDG: Integration of Heterogeneous Space and Gaussian Kernel for Domain-Generalized Nuclei Segmentation
Zhenye Lou, Qing Xu, Zekun Jiang +6
Domain-generalized nuclei segmentation refers to the generalizability of models to unseen domains based on knowledge learned from source domains and is challenged by various image…
Pushing the limits of cell segmentation models for imaging mass cytometry
Kimberley M. Bird, Xujiong Ye, Alan M. Race +1
Imaging mass cytometry (IMC) is a relatively new technique for imaging biological tissue at subcellular resolution. In recent years, learning-based segmentation methods have enable…
DenResCov-19: A deep transfer learning network for robust automatic classification of COVID-19, pneumonia, and tuberculosis from X-rays
Michail Mamalakis, Andrew J. Swift, Bart Vorselaars +6
The global pandemic of COVID-19 is continuing to have a significant effect on the well-being of global population, increasing the demand for rapid testing, diagnosis, and treatment…
DR-Unet104 for Multimodal MRI brain tumor segmentation
Jordan Colman, Lei Zhang, Wenting Duan +1
In this paper we propose a 2D deep residual Unet with 104 convolutional layers (DR-Unet104) for lesion segmentation in brain MRIs. We make multiple additions to the Unet architectu…
Endoscopy disease detection challenge 2020
Sharib Ali, Noha Ghatwary, Barbara Braden +7
Whilst many technologies are built around endoscopy, there is a need to have a comprehensive dataset collected from multiple centers to address the generalization issues with most…