Titlebook: Domain Adaptation and Representation Transfer; 4th MICCAI Workshop, Konstantinos Kamnitsas,Lisa Koch,Sotirios Tsaftari Conference proceedin

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https://doi.org/10.1007/978-3-031-16640-2growth of cells according to cytotoxicity grade under the microscope. Thus, human fatigue plays a role in error making, making the use of deep learning appealing. Due to the high cost of training data annotation, an approach without manual annotation is needed. We propose ., a new method for trainin

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Understanding Workplace Relationshipstraining and testing. We explore how such methods perform in a task-agnostic setting that more closely resembles dynamic clinical environments with gradual population shifts. We propose ODEx, a holistic solution that combines out-of-distribution detection with continual learning techniques. Validati

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Ajay Mehra,Diane Kang,Evgenia Dolgova of the background pixels often dominates the BN statistics because the background accounts for a large proportion of the entire image. This paper focuses on enhancing BN with the intensity distribution of foreground pixels, the one that really matters for image segmentation. We propose a new normal

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,Benchmarking and Boosting Transformers for Medical Image Classification, imaging: (1) good initialization is more crucial for transformer-based models than for CNNs, (2) self-supervised learning based on masked image modeling captures more generalizable representations than supervised models, and (3) assembling a larger-scale domain-specific dataset can better bridge th

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