Výsledky vyhledávání - "Rebelatto, Marlon"

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Auxiliary CycleGAN-guidance for Task-Aware Domain Translation from Duplex to Monoplex IHC Images

Autor: Brieu, Nicolas, Triltsch, Nicolas, Wortmann, Philipp, Winter, Dominik, Saran, Shashank, Rebelatto, Marlon, Schmidt, Günter

Generative models enable the translation from a source image domain where readily trained models are available to a target domain unseen during training. While Cycle Generative Adversarial Networks (GANs) are well established, the associated cycle co

Externí odkaz: http://arxiv.org/abs/2403.07389

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DASGAN -- Joint Domain Adaptation and Segmentation for the Analysis of Epithelial Regions in Histopathology PD-L1 Images

Autor: Kapil, Ansh, Wiestler, Tobias, Lanzmich, Simon, Silva, Abraham, Steele, Keith, Rebelatto, Marlon, Schmidt, Guenter, Brieu, Nicolas

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. We introduce here a novel deep learning solution

Externí odkaz: http://arxiv.org/abs/1906.11118

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Deep Semi Supervised Generative Learning for Automated PD-L1 Tumor Cell Scoring on NSCLC Tissue Needle Biopsies

Autor: Kapil, Ansh, Meier, Armin, Zuraw, Aleksandra, Steele, Keith, Rebelatto, Marlon, Schmidt, Günter, Brieu, Nicolas

Publikováno v: Scientific Reports, volume 8, Article number: 17343 (2018)

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. The quantification of PD-L1 expression current

Externí odkaz: http://arxiv.org/abs/1806.11036

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