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Testing Segmentation Popular Loss and Variations in Three Multiclass Medical Imaging Problems
2021
Journal of Imaging
Image structures are segmented automatically using deep learning (DL) for analysis and processing. The three most popular base loss functions are cross entropy (crossE), intersect-over-the-union (IoU), and dice. Which should be used, is it useful to consider simple variations, such as modifying formula coefficients? How do characteristics of different image structures influence scores? Taking three different medical image segmentation problems (segmentation of organs in magnetic resonance
doi:10.3390/jimaging7020016
pmid:34460615
pmcid:PMC8321275
fatcat:nfizwg2kunhqdc5etxcqzs7biu