19 citations · 37 across the 3 of their papers we have counts for
3 papers
eess.IV2019★ 13 cited
Domain Adaptation-based Augmentation for Weakly Supervised Nuclei Detection
Nicolas Brieu, Armin Meier, Ansh Kapil +4
The detection of nuclei is one of the most fundamental components of computational pathology. Current state-of-the-art methods are based on deep learning, with the prerequisite tha…
eess.IV2019★ 19 cited
DASGAN -- Joint Domain Adaptation and Segmentation for the Analysis of Epithelial Regions in Histopathology PD-L1 Images
Ansh Kapil, Tobias Wiestler, Simon Lanzmich +5
The analysis of the tumor environment on digital histopathology slides is becoming key for the understanding of the immune response against cancer, supporting the development of no…
cs.CV2017★ 5 cited
Learning without Prejudice: Avoiding Bias in Webly-Supervised Action Recognition
Christian Rupprecht, Ansh Kapil, Nan Liu +2
Webly-supervised learning has recently emerged as an alternative paradigm to traditional supervised learning based on large-scale datasets with manual annotations. The key idea is…