ConMIL uses per-class interpretable multiple instance learning and conformal prediction to feed LLMs calibrated, highlighted hints, lifting accuracy on ECG and EEG visual inspection from 13 to 48 percent to 71 to 97 percent.
TimeMIL: Advancing multivariate time series classification via a time-aware multiple instance learning
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Smarter Together: Combining Large Language Models and Small Models for Physiological Signals Visual Inspection
ConMIL uses per-class interpretable multiple instance learning and conformal prediction to feed LLMs calibrated, highlighted hints, lifting accuracy on ECG and EEG visual inspection from 13 to 48 percent to 71 to 97 percent.