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Berlin Institute for the Foundations of Learning and Data, Berlin, Germany, Machine Learning Group, Technische Universität Berlin, Berlin, Germany
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Symb-xMIL transforms MIL interpretability by revealing the hidden logical rules that govern model predictions, exposing decision patterns that traditional heatmaps miss.
Pretraining on diverse synthetic data allows a single model to excel across multiple MIL tasks with minimal labeled input, outperforming conventional supervised approaches.
Attention heatmaps in MIL models for histopathology are often misleading, and simpler methods like perturbation or LRP provide more faithful explanations.
