Institució Catalana de Recerca i Estudis Avançats

Saliency maps for convolutional classifiers on medical images now have a new training route: SalientNet needs no annotated saliency ground truth and instead requires the generated mask to keep enough information for a frozen classifier to reproduce its original prediction within a controlled tolerance. Across BreakHis, Brain MRI ND5 and FracAtlas, it reaches higher faithfulness (Win %) and lower prediction Drop (%) than Grad-CAM, Grad-CAM++ and LIME in nearly all dataset-architecture combinations, and on FracAtlas its maps correlate spatially with practitioner-annotated fracture regions, with a mean ground-truth active fraction of up to 0.96 for ResNet-18.

Saliency maps for convolutional classifiers on medical images now have a new training route: SalientNet needs no annotated saliency ground truth and instead requires the generated mask to keep enough information for a frozen classifier to reproduce its original prediction within a controlled tolerance. Across BreakHis, Brain MRI ND5 and FracAtlas, it reaches higher faithfulness (Win %) and lower prediction Drop (%) than Grad-CAM, Grad-CAM++ and LIME in nearly all dataset-architecture combinations, and on FracAtlas its maps correlate spatially with practitioner-annotated fracture regions, with a mean ground-truth active fraction of up to 0.96 for ResNet-18.