# Saliency Maps

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.

A cross-architecture comparison of CNNs, ViTs and Vision Mamba uses human eye-tracking heatmaps as an independent benchmark to quantify how closely model saliency matches human gaze, finding that Mamba reaches the highest localization scores on chest X-rays while its saliency intensity is nearly uncorrelated with human fixations.

A cross-architecture comparison of CNNs, ViTs and Vision Mamba uses human eye-tracking heatmaps as an independent benchmark to quantify how closely model saliency matches human gaze, finding that Mamba reaches the highest localization scores on chest X-rays while its saliency intensity is nearly uncorrelated with human fixations.

This study evaluated 11 vision models from the ResNet, DenseNet, and Vision Transformer families (seven trained from scratch, four pretrained) on the Oxford-IIIT Pet and Chest X-ray Pneumothorax datasets with ground-truth segmentation masks, generated explanations with five post-hoc XAI methods (Saliency, GradientSHAP, Integrated Gradients, Feature Permutation, Grad-CAM), and quantified mask alignment using Relevance Rank Accuracy and the proposed Dual-Polarity Precision; it found that increasing depth and parameter count did not improve explanation quality in most statistical comparisons, that smaller models often matched or exceeded deeper variants, that pretraining typically improved predictive performance and increased the dependence of explanations on learned weights without consisten

This study evaluated 11 vision models from the ResNet, DenseNet, and Vision Transformer families (seven trained from scratch, four pretrained) on the Oxford-IIIT Pet and Chest X-ray Pneumothorax datasets with ground-truth segmentation masks, generated explanations with five post-hoc XAI methods (Saliency, GradientSHAP, Integrated Gradients, Feature Permutation, Grad-CAM), and quantified mask alignment using Relevance Rank Accuracy and the proposed Dual-Polarity Precision; it found that increasing depth and parameter count did not improve explanation quality in most statistical comparisons, that smaller models often matched or exceeded deeper variants, that pretraining typically improved predictive performance and increased the dependence of explanations on learned weights without consisten