JDCNet improves X-ray classification by gating CT distillation to high-confidence samples, beating the supervised baseline and other distillation methods on a 510-patient paired cohort with 5-fold CV.
Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
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JDCNet: Confidence-Gated Privileged-Modality Distillation for Cost-Preserving X-ray Inference
JDCNet improves X-ray classification by gating CT distillation to high-confidence samples, beating the supervised baseline and other distillation methods on a 510-patient paired cohort with 5-fold CV.