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Found 2 papers, 2 papers with code
Average Calibration Error: A Differentiable Loss for Improved Reliability in Image Segmentation
Using mL1-ACE, we reduce average and maximum calibration error by 45% and 55% respectively, maintaining a Dice score of 87% on the BraTS 2021 dataset.
Multiple Instance Learning with Auxiliary Task Weighting for Multiple Myeloma Classification
To aid radiological reading, we propose an auxiliary task-based multiple instance learning approach (ATMIL) for MM classification with the ability to localize sites of disease.
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