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- W4367848900 abstract "<p>In this paper, we highlight three issues that limit performance of machine learning on biomedical images, and tackle them through 3 case studies: 1) Interactive Machine Learning (IML): we show how IML can drastically improve exploration time and quality of direct volume rendering. 2) transfer learning: we show how transfer learning along with intelligent pre-processing can result in better Alzheimer's diagnosis using a much smaller training set 3) data imbalance: we show how our novel focal Tversky loss function can provide better segmentation results taking into account the imbalanced nature of segmentation datasets. The case studies are accompanied by in-depth analytical discussion of results with possible future directions.</p>" @default.
- W4367848900 created "2023-05-04" @default.
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- W4367848900 date "2023-05-03" @default.
- W4367848900 modified "2023-09-27" @default.
- W4367848900 title "Machine Learning on Biomedical Images: Interactive Learning, Transfer Learning, Class Imbalance, and Beyond" @default.
- W4367848900 doi "https://doi.org/10.32920/22734299" @default.
- W4367848900 hasPublicationYear "2023" @default.
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