arXiv AI
Scores That Hold, Benchmarks That Leak: Measuring Dataset Contamination in Public Brain-Tumor MRI Classification
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv Machine Learning
Jul 15
Auditing Data Leakage in Whole-Slide Image Multimodal Benchmarks
arXiv:2607. 12278v1 Announce Type: cross Abstract: Recent vision-language models (VLMs) for computational pathology report striking zero-shot performance on whole-slide image (WSI) visual question answering (VQA) benchmarks.
arXiv Computer Vision
Sep 1
On the Role of MRI Sequences in Cross-Dataset Generalization for Brain Tumor Segmentation
arXiv:2608.29944v1 Announce Type: new Abstract: Brain tumor segmentation in magnetic resonance imaging (MRI) is a critical task for diagnosis and treatment planning. Despite the success of deep learn...
arXiv Machine Learning
Jul 22
Cross-Dataset Generalization in Breast MRI Tumor Classification via Class-Wise Dataset Mixing
arXiv:2607. 18678v1 Announce Type: cross Abstract: Breast MRI is highly sensitive for detecting breast tumors, but exams contain many slices and require substantial reading time.
arXiv AI
Sep 15
Anatomical Grounding and Leakage-Aware Multimodal Contrastive Learning for Alzheimer's Disease Classification from Structural MRI
arXiv:2609.15888v1 Announce Type: cross Abstract: Deep networks trained on structural MRI for Alzheimer's disease (AD) staging often reach reasonable accuracy while attending to anatomically irreleva...
arXiv Computer Vision
Aug 31
Medical Imaging AI Competitions Lack Fairness
Benchmarking competitions are central to AI development in medical imaging, but it is unclear if they provide representative, accessible, and reusable data for clinical relevance. This study systematically examined 249 challenges (458 tasks) across 19 imaging modalities, finding limited geographic, modality, and problem-type representation. Additionally, many datasets suffer from restrictive access, ambiguous licensing, and poor documentation, hindering reproducibility and long-term reuse.
arXiv AI
Sep 15
GRIN+: Towards Fast Yet Effective Machine Unlearning for Imbalanced Medical Data
GRIN+ is a new machine unlearning framework that targets fast and precise data erasure in imbalanced medical datasets. It separates unlearning‑specific knowledge from general representations by analyzing gradient contributions of forget and retain sets, introduces a class‑adaptive influence scoring to counter gradient dominance, and uses a direction‑constrained update to protect essential clinical knowledge. Benchmarks on skin cancer, brain tumor, and breast ultrasound data show that GRIN+ balances privacy, efficiency, and utility, achieving high diagnostic accuracy and faster runtime than existing methods.