QG-MIL introduces four gated transformer components that yield +6.1 average macro F1 improvement over baselines on six whole-slide and cell-level medical imaging benchmarks while producing more uniform attention.
Cost-sensitive multi-kernel elm based on reduced expectation kernel auto-encoder.PLOS ONE, 20 (2):1–20, 02 2025
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QG-MIL: A Gated Transformer Aggregator for Domain-Agnostic Multiple Instance Learning in Medical Imaging
QG-MIL introduces four gated transformer components that yield +6.1 average macro F1 improvement over baselines on six whole-slide and cell-level medical imaging benchmarks while producing more uniform attention.