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histolab: A Python library for reproducible Digital Pathology preprocessing with automated testing.

, , , , , and . SoftwareX, (December 2022)

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Joint region and nucleus segmentation for characterization of tumor infiltrating lymphocytes in breast cancer., , , , , , , , , and 1 other author(s). Medical Imaging: Digital Pathology, volume 10956 of SPIE Proceedings, page 109560M. SPIE, (2019)Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models, , , , , , , , , and 1 other author(s). Scientific Reports, 7 (1): 11707-- (2017)histolab: A Python library for reproducible Digital Pathology preprocessing with automated testing., , , , , and . SoftwareX, (December 2022)An Ensemble-based Active Learning for Breast Cancer Classification., , , , , and . BIBM, page 2549-2553. IEEE, (2019)Explainable nucleus classification using Decision Tree Approximation of Learned Embeddings., , , , , , , , , and 1 other author(s). Bioinform., 38 (2): 513-519 (2022)NuCLS: A scalable crowdsourcing, deep learning approach and dataset for nucleus classification, localization and segmentation., , , , , , , , , and 27 other author(s). CoRR, (2021)Structured crowdsourcing enables convolutional segmentation of histology images., , , , , , , , , and 21 other author(s). Bioinform., 35 (18): 3461-3467 (2019)A Histopathology Study Comparing Contrastive Semi-Supervised and Fully Supervised Learning., , and . CoRR, (2021)HistomicsML2.0: Fast interactive machine learning for whole slide imaging data., , , , , , , , and . CoRR, (2020)Variable speed micro-hydro power plant: Modelling, losses analysis, and experiment validation., , , and . ICIT, page 1079-1084. IEEE, (2018)