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[2602.22955] MM-NeuroOnco: A Multimodal Benchmark and Instruction Dataset for MRI-Based Brain Tumor Diagnosis
Summary
The article presents MM-NeuroOnco, a comprehensive dataset aimed at improving MRI-based brain tumor diagnosis through multimodal instructions and benchmarks.
Why It Matters
This research addresses the limitations of existing datasets in medical imaging by providing a rich, annotated resource that enhances diagnostic accuracy and understanding of brain tumors. It highlights the need for improved models in a critical healthcare area, potentially leading to better patient outcomes.
Key Takeaways
- MM-NeuroOnco includes 24,726 MRI slices and 200,000 multimodal instructions.
- The dataset aims to improve diagnostic reasoning in brain tumor detection.
- Current models show only 41.88% accuracy, indicating significant challenges.
- NeuroOnco-GPT demonstrates a 27% improvement in diagnostic accuracy.
- The dataset and code are publicly available for further research.
Computer Science > Computer Vision and Pattern Recognition arXiv:2602.22955 (cs) [Submitted on 26 Feb 2026] Title:MM-NeuroOnco: A Multimodal Benchmark and Instruction Dataset for MRI-Based Brain Tumor Diagnosis Authors:Feng Guo, Jiaxiang Liu, Yang Li, Qianqian Shi, Mingkun Xu View a PDF of the paper titled MM-NeuroOnco: A Multimodal Benchmark and Instruction Dataset for MRI-Based Brain Tumor Diagnosis, by Feng Guo and 4 other authors View PDF HTML (experimental) Abstract:Accurate brain tumor diagnosis requires models to not only detect lesions but also generate clinically interpretable reasoning grounded in imaging manifestations, yet existing public datasets remain limited in annotation richness and diagnostic semantics. To bridge this gap, we introduce MM-NeuroOnco, a large-scale multimodal benchmark and instruction-tuning dataset for brain tumor MRI understanding, consisting of 24,726 MRI slices from 20 data sources paired with approximately 200,000 semantically enriched multimodal instructions spanning diverse tumor subtypes and imaging modalities. To mitigate the scarcity and high cost of diagnostic semantic annotations, we develop a multi-model collaborative pipeline for automated medical information completion and quality control, enabling the generation of diagnosis-related semantics beyond mask-only annotations. Building upon this dataset, we further construct MM-NeuroOnco-Bench, a manually annotated evaluation benchmark with a rejection-aware setting to reduce bi...
