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

📅 2026-09-02
📈 Citations: 0
Influential: 0
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🤖 AI Summary
研究解决了脑肿瘤MRI报告中诊断不准确的问题,通过引入NeuroFusion系统利用冻结的分割特征提高诊断准确性。
📝 Abstract
A capable brain-MRI report generator can still be, in effect, diagnostically silent. When a multi-chain chain-of-thought (CoT) reporter built on a medical Mistral-7B backbone is evaluated on held-out cohorts, it names most meningiomas and almost all metastases "glioma" (diagnosis recall 0.44/0.07). Yet the answer is not absent from the model: a supervised linear probe applied to its frozen segmentation features recovers the three tumour cohorts at 0.82 macro-F$_1$ (5-fold cross-validation; chance $\approx$0.33). We introduce NeuroFusion, an assistive reporter that surfaces this latent signal rather than overriding it: discriminative field-classifier heads over per-lesion features condition a fast, single-pass draft-then-review decoder on their committed outputs. Built on the identical Mistral backbone, this restores the diagnosis (meningioma 0.92, metastasis 0.75) and wins 8 of 9 prose-content comparisons across three held-out cohorts (RaTEScore, RadGraph-F$_1$, GREEN; Holm-corrected paired BCa), with no significant loss on the ninth, at 5-6x lower latency ($\approx$80 vs. 457 s/case). A controlled negative result sharpens the mechanism: a learned diagnosis pin that overrides the decoder instead of merely informing it collapses out-of-distribution metastasis recall to 0.03. Grammar-constrained decoding keeps 92.3% of records schema-valid, making every sentence entailment-checkable (7.5% contradicted vs. 36.8% for the direct baseline). In a blinded nine-case pilot, two board-certified neurologists independently rated NeuroFusion highest in every tumour type, the only system with zero critical errors, and gave it the top-rated sign-off in eight of nine cases (six outright, two ties).
Problem

Research questions and friction points this paper is trying to address.

brain-MRI
diagnosis recall
latent signal
meningioma
metastasis
Innovation

Methods, ideas, or system contributions that make the work stand out.

NeuroFusion
latent signal surfacing
discriminative field-classifier heads
draft-then-review decoder
grammar-constrained decoding
K
Khawaja Murad ul Hassan
National University of Sciences and Technology, Islamabad, Pakistan
R
Ruqiyya Adil
National University of Sciences and Technology, Islamabad, Pakistan
A
Adil Qayyum
Consultant Radiologist, Rawalpindi, Pakistan
R
Rida Hassan
Bahria University Health Sciences Campus, Islamabad, Pakistan
Asad Mansoor Khan
Asad Mansoor Khan
National University of Sciences and Technology, Islamabad, Pakistan
Muhammad Usman Akram
Muhammad Usman Akram
National University of Sciences and Technology, Islamabad, Pakistan
Mehran Ebrahimi
Mehran Ebrahimi
Ontario Tech University
Medical Image ProcessingComputer VisionInverse Problems