arXiv AI
Sep 16

A Vision-Language Foundation Model for Precise and Comprehensive Brain Tumor Diagnosis from Preoperative Multimodal Data

arXiv:2609.16597v1 Announce Type: cross Abstract: Background Non-invasive presurgical diagnosis of brain tumor types from Magnetic Resonance Imaging (MRI) is essential but challenging due to overlapp...

By Yinong Wang (Joyce), Jianwen Chen (Joyce), Zhou Chen (Joyce), Shuwen Kuang (Joyce), Haoning Jiang (Joyce), Yanzhao Shi (Joyce), Huichun Yuan (Joyce), Yan-ran (Joyce), Wang, Bing Wang, Lei Wu, Bin Tang, Li Meng, Baihua Luo, Bin Zhou, Wei Ding, Weiming Zhong, Wei Hou, Yuanbing Chen, Zhiping Wan, Wei Wang, Zhenkun Xiao, Wenwu Wan, Allen He, Yuyin Zhou, Longbo Zhang, Feifei Wang, Zhixiong Liu, Michael Iv, Xuan Gong, Liangqiong Qu
arXiv Computer Vision
Sep 3

The Diagnosis a Reporter Leaves Unspoken: Surfacing Frozen Tumor Features for Brain-Tumor MRI Reporting

The paper introduces NeuroFusion, an assistive brain‑MRI report generator that surfaces latent tumor signals from a frozen Mistral‑7B backbone. By adding discriminative field‑classifier heads over per‑lesion features, NeuroFusion restores accurate diagnoses (meningioma 0.92, metastasis 0.75) and improves prose quality while reducing latency 5–6×. A controlled negative result shows that overriding the decoder with a learned diagnosis pin harms performance, and grammar‑constrained decoding yields high schema‑validity (92.3%).

By Khawaja Murad ul Hassan, Ruqiyya Adil, Adil Qayyum, Rida Hassan, Asad Mansoor Khan, Muhammad Usman Akram, Mehran Ebrahimi
arXiv AI
6d ago

OpenMTB-Audit: Exposing Over-Refusal and Clinical Expert Perspectives in LLM-Based Molecular Tumor Board Safety Evaluation

OpenMTB‑Audit is an open‑source benchmark that tests large language models on 500 synthetic non‑small cell lung cancer cases, covering five adversarial error categories and four safety labels: Supported, Partially Supported, Unsupported, and Insufficient Information. The study found that all eight tested LLMs over‑refused Partially Supported recommendations, collapsing labels to achieve high safety scores. A deterministic seven‑module framework, MTB‑AuditAgent, was introduced to reduce over‑refusal to 6.7% and reach 91.2% accuracy, while an oncologist annotation study highlighted disagreement around the boundary between information sufficiency and treatment optimization.

By Negin Ashrafi, Jia Luo, Stacey M. Frumm, Roxana Daneshjou