Titlebook: Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis; MICCAI 2021 Challeng Marc Aubreville,David Zimmerer,

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Domain Adversarial RetinaNet as?a?Reference Algorithm for?the?MItosis DOmain Generalization Challenglity and reduce labeling time. These systems, however, are generally highly dependent on their training domain and show poor applicability to unseen domains. In histopathology, these domain shifts can result from various sources, including different slide scanning systems used to digitize histologic

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Assessing Domain Adaptation Techniques for?Mitosis Detection in?Multi-scanner Breast Cancer Histopatts manually count the number of dividing cells (mitotic figures) in biopsy or tumour resection specimens. Since the process is subjective and time-consuming, data-driven artificial intelligence (AI) methods have been developed to automatically detect mitotic figures. However, these methods often gen

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Stain-Robust Mitotic Figure Detection for?the?Mitosis Domain Generalization Challengewith tumour grading. The MItosis DOmain Generalization (MIDOG) challenge aims to test the robustness of detection models on unseen data from multiple scanners for this task. We present a short summary of the approach employed by the . team to address this challenge. Our approach is based on a hybrid

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