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Combining Superpixels and Deep Learning Approaches to Segment Active Organs in Metastatic Breast Cancer PET Images*., , , , , , , , , and 1 other author(s). EMBC, page 1536-1539. IEEE, (2020)Development of an absolute assignment predictor for triple-negative breast cancer subtyping using machine learning approaches., , , , , , , , , and 1 other author(s). Comput. Biol. Medicine, (2021)Deep learning approaches for bone and bone lesion segmentation on 18FDG PET/CT imaging in the context of metastatic breast cancer*., , , , , , , , , and 1 other author(s). EMBC, page 1532-1535. IEEE, (2020)Pertuzumab, Trastuzumab, and Docetaxel in HER2-Positive Metastatic Breast Cancer, , , , , , , , , and 4 other author(s). New England Journal of Medicine, (February 2015)bc-GenExMiner 3.0: new mining module computes breast cancer gene expression correlation analyses., , , , , , and . Database J. Biol. Databases Curation, (2013)Random forest of perfect trees: concept, performance, applications and perspectives., , , , , , , , , and 1 other author(s). Bioinform., 37 (15): 2165-2174 (2021)Comparison between threshold-based and deep learning-based bone segmentation on whole-body CT images., , , , , , , , , and 1 other author(s). Medical Imaging: Computer-Aided Diagnosis, volume 11597 of SPIE Proceedings, SPIE, (2021)Influence of inputs for bone lesion segmentation in longitudinal 18F-FDG PET/CT imaging studies., , , , , , , , , and 2 other author(s). EMBC, page 4736-4739. IEEE, (2022)