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Virtual Biopsy for Intracranial Tumors Diagnosis on MRI

arXiv 2602.21613 ↗First posted 25 Feb 2026cs.CV cs.AIcited by 0

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Abstract (arXiv)

Deep intracranial tumors situated in eloquent brain regions controlling vital functions present critical diagnostic challenges. Clinical practice has shifted toward stereotactic biopsy for pathological confirmation before treatment. Yet biopsy carries inherent risks of hemorrhage and neurological deficits and struggles with sampling bias due to tumor spatial heterogeneity, because pathological changes are typically region-selective rather than tumor-wide. Therefore, advancing non-invasive MRI-based pathology prediction is essential for holistic tumor assessment and modern clinical decision-making. The primary challenge lies in data scarcity: low tumor incidence requires long collection cycles, and annotation demands biopsy-verified pathology from neurosurgical experts. Additionally, tiny lesion volumes lacking segmentation masks cause critical features to be overwhelmed by background noise. To address these challenges, we construct the ICT-MRI dataset - the first public biopsy-verified benchmark with 249 cases across four categories. We propose a Virtual Biopsy framework comprising: MRI-Processor for standardization; Tumor-Localizer employing vision-language models for coarse-to-fine localization via weak supervision; and Adaptive-Diagnoser with a Masked Channel Attention mechanism fusing local discriminative features with global contexts. Experiments demonstrate over 90% accuracy, outperforming baselines by more than 20%.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 13 of its 29 references that are in the map. First posted on arXiv 25 Feb 2026.

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13 of 29 references.

References · 13

  1. 2016Densely Connected Convolutional Networkscited by 6,691
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  10. 2022Masked Image Modeling Advances 3D Medical Image Analysiscited by 71
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  12. 2024MedSAM-U: Uncertainty-Guided Auto Multi-Prompt Adaptation for Reliable MedSAMcited by 17
1 more
  1. 2024Residual Vision Transformer (ResViT) Based Self-Supervised Learning Model for Brain Tumor Classificationcited by 2

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  4. 2025NOVA: A Benchmark for Anomaly Localization and Clinical Reasoning in Brain MRI
  5. 2026NeuroVLM-Bench: Evaluation of Vision-Enabled Large Language Models for Clinical Reasoning in Neurological Disorders
  6. 2026NeuroMosaic: Anatomically Grounded Multimodal Large Language Modeling for Molecularly Aware Glioma Reasoning from 3D MRI and Clinical Narratives
  7. 2026A Unified Benchmark of Deep Learning Models for Multi-task 3D Brain Tumor Segmentation from Magnetic Resonance Imaging
  8. 2024Advancing Efficient Brain Tumor Multi-Class Classification -- New Insights from the Vision Mamba Model in Transfer Learning