Tumour-Guided Attention with Mask-Supervised Pooling for Leakage-Aware and Explainable Brain Tumour Classification in MRI: A Reproducibility and Evaluation Study

Brain tumour classifiers are often reported as near-perfect and explainable without checking patient structure or annotations. We reproduced a recent attention framework (SSPANet) on the Figshare dataset (3064 T1-weighted contrast-enhanced slices, 233 patients). Its image-level split is fully patient-leaky; every configuration exceeds 98% accuracy, which drops by 5–9 points on the patient-wise split. Under a fixed training recipe, seven attention modules stayed within ±0.63 points of the plain backbone, whereas the recipe changed accuracy by up to 4.7 points. As a case study, TG-SSPANet uses expert masks to supervise attention, train a segmentation decoder and gate evidence pooling. With ResNet50, it reached 96.8% slice-level and, averaging slice probabilities, 98.3% patient-level accuracy (84.5% if every slice had to be correct; 15% of patients had discordant slices), within the confidence intervals of simpler mask-using pipelines; its attention module added no measurable benefit. Its Grad-CAM++ maps hit the tumour in 81% of held-out slices, but this follows from its decoder by construction, not the classifier’s reasoning. After tumour inpainting, whole-slice models kept 80–86% accuracy, and shuffling the remaining tissue left 60–74%, so location, perilesional cues and tissue statistics cannot be separated. Patient-wise evaluation, same-recipe controls and mask-based faithfulness tests should precede explainability claims.