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Found 6 papers, 0 papers with code
Novel Deep Learning Approach to Derive Cytokeratin Expression and Epithelium Segmentation from DAPI
Generative Adversarial Networks (GANs) are state of the art for image synthesis.
Stain Isolation-based Guidance for Improved Stain Translation
Unsupervised and unpaired domain translation using generative adversarial neural networks, and more precisely CycleGAN, is state of the art for the stain translation of histopathology images.
Domain Adaptation-based Augmentation for Weakly Supervised Nuclei Detection
The detection of nuclei is one of the most fundamental components of computational pathology.
DASGAN -- Joint Domain Adaptation and Segmentation for the Analysis of Epithelial Regions in Histopathology PD-L1 Images
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 novel immuno-therapies.
Deep Semi Supervised Generative Learning for Automated PD-L1 Tumor Cell Scoring on NSCLC Tissue Needle Biopsies
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 PD-1/PD-L1 treatments.
Learning without Prejudice: Avoiding Bias in Webly-Supervised Action Recognition
One of the main problems in webly-supervised learning is cleaning the noisy labeled data from the web.
