A two-branch MIL training method with self-confidence losses and attention calibration improves bag- and instance-level whole-slide image classification on CAMELYON16 and TCGA-NSCLC over WENO and MHIM-MIL baselines.
Deep neural network models for computational histopathology: A survey,
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Denoising Mutual Knowledge Distillation in Bi-Directional Multiple Instance Learning
A two-branch MIL training method with self-confidence losses and attention calibration improves bag- and instance-level whole-slide image classification on CAMELYON16 and TCGA-NSCLC over WENO and MHIM-MIL baselines.