102 citations · 142 across the 6 of their papers we have counts for
6 papers
Novel Deep Learning Approach to Derive Cytokeratin Expression and Epithelium Segmentation from DAPI
Felix Jakob Segerer, Katharina Nekolla, Lorenz Rognoni +4
Generative Adversarial Networks (GANs) are state of the art for image synthesis. Here, we present dapi2ck, a novel GAN-based approach to synthesize cytokeratin (CK) staining from i…
Stain Isolation-based Guidance for Improved Stain Translation
Nicolas Brieu, Felix J. Segerer, Ansh Kapil +2
Unsupervised and unpaired domain translation using generative adversarial neural networks, and more precisely CycleGAN, is state of the art for the stain translation of histopathol…
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…
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…
Deep Semi Supervised Generative Learning for Automated PD-L1 Tumor Cell Scoring on NSCLC Tissue Needle Biopsies
Ansh Kapil, Armin Meier, Aleksandra Zuraw +4
The level of PD-L1 expression in immunohistochemistry (IHC) assays is a key biomarker for the identification of Non-Small-Cell-Lung-Cancer (NSCLC) patients that may respond to anti…
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…