arXiv Computer Vision
BrainDiff: Longitudinal Report Generation for Multimodal Brain MRI
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arXiv Computer Vision
Sep 17
Adaptive Temporal Gating of Longitudinal Magnetic Resonance Imaging for Dementia Prediction
arXiv:2605.28397v2 Announce Type: replace Abstract: Predicting which people with mild cognitive impairment will develop dementia matters for early treatment. Yet structural imaging models have relied...
arXiv Machine Learning
Aug 31
Prompts Without Evidence: How Neuroimaging Mentions Shift Clinical Vision-Language Model Predictions
arXiv:2603.28387v3 Announce Type: replace-cross Abstract: Trustworthy clinical AI must use real evidence and avoid relying on surface-level artifacts. We evaluate 12 open-weight vision-language model...
arXiv AI
Jun 19
The Scaffold Effect: How Prompt Framing Drives Apparent Multimodal Gains in Clinical VLM Evaluation
arXiv:2603. 28387v2 Announce Type: replace Abstract: Trustworthy clinical AI requires that performance gains reflect genuine evidence integration rather than surface-level artifacts.
arXiv AI
Jun 17
Vision-language models for chest radiography do not always need the image
arXiv:2606. 17710v1 Announce Type: cross Abstract: Medical vision-language models report strong chest radiograph accuracy, and this is increasingly read as evidence that they use the image.
arXiv Computer Vision
Sep 11
Brain-PACE: A Deep Siamese MRI Framework for Modelling Longitudinal Brain Acceleration
Brain-PACE is a deep Siamese MRI framework that directly estimates the pace of structural brain ageing from paired T1‑weighted MRI scans, extending the LILAC model with spatial attention, soft label distribution learning, and a Cramér distance objective. In a study of participants with mild cognitive impairment, 42.6 % showed accelerated ageing, and faster Brain‑PACE scores correlated with greater functional and cognitive impairment as well as higher regional tau burden in key brain regions. The method improves probabilistic performance, reduces prediction bias, and provides predictive uncertainty, offering a complementary longitudinal imaging phenotype sensitive to early neurodegeneration.
arXiv AI
Oct 1
GateSPINE: Gated Cross-View Fusion for Lumbar Spine MRI Report Generation
GateSPINE is a vision‑language framework designed for automated lumbar spine MRI report generation. It fuses sagittal T1 and T2 volumes using a training‑free gated cross‑view fusion module, then encodes the fused sagittal and axial volumes with parallel 3D encoders before decoding the combined representation into a report. Evaluated on three datasets, GateSPINE achieves the highest clinical efficacy F1 scores, improving recall across all datasets while remaining competitive on standard natural language generation metrics.