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xu yin

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NeurIPS Conference 2025 Conference Paper

DrVD-Bench: Do Vision-Language Models Reason Like Human Doctors in Medical Image Diagnosis?

Tianhong Zhou
xu yin
Yingtao Zhu
Chuxi Xiao
Haiyang Bian
Lei Wei
Xuegong Zhang

Vision–language models (VLMs) exhibit strong zero-shot generalization on natural images and show early promise in interpretable medical image analysis. However, existing benchmarks do not systematically evaluate whether these models truly reason like human clinicians or merely imitate superficial patterns. To address this gap, we propose DrVD-Bench, the first multimodal benchmark for clinical visual reasoning. DrVD-Bench consists of three modules: Visual Evidence Comprehension, Reasoning Trajectory Assessment, and Report Generation Evaluation, comprising a total of 7, 789 image–question pairs. Our benchmark covers 20 task types, 17 diagnostic categories, and five imaging modalities—CT, MRI, ultrasound, radiography, and pathology. DrVD-Bench is explicitly structured to reflect the clinical reasoning workflow from modality recognition to lesion identification and diagnosis. We benchmark 19 VLMs, including general-purpose and medical-specific, open-source and proprietary models, and observe that performance drops sharply as reasoning complexity increases. While some models begin to exhibit traces of human-like reasoning, they often still rely on shortcut correlations rather than grounded visual understanding. DrVD-Bench offers a rigorous and structured evaluation framework to guide the development of clinically trustworthy VLMs.

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EAAI Journal 2024 Journal Article

A novel semi-supervised model for pre-impact fall detection with limited fall data

Xiaoqun Yu
Jiansong Wan
Guoyuan An
xu yin
Shuping Xiong

Due to the high prevalence and severe consequences of falls among older people, pre-impact fall detection algorithms which aim to detect falls before body-ground impacts are of critical importance. The success of existing algorithms based on wearable sensors relies heavily on numerous labeled data from both falls and activities of daily living (ADLs). However, fall data is much more difficult to acquire compared with ADLs, and moreover, labeling falling period apart from ADLs is laborious and time-consuming. To this end, we proposed a novel semi-supervised model for pre-impact fall detection, called Semi-PFD, which maximizes the utilization of easily obtainable ADL data and reduces the dependency on labeled fall data. This model was evaluated on two large-scale public fall datasets (KFall and SisFall) and compared with its supervised baseline under different ratios of fall data. Cross-validation results showed that Semi-PFD outperformed the supervised baseline across all conditions on both datasets. More importantly, Semi-PFD achieved considerable improvement of 2–4% F1-score when the ratios of fall data were low (≤23. 1%). Remarkably, with only 76. 9% of fall data, Semi-PFD achieved comparable and even higher F1-scores than the state-of-the-art benchmark models which utilized 100% of fall data. We further observed that the incorporation of unsupervised training in Semi-PFD mitigated the typical overconfidence problem associated with supervised training without affecting lead time. These findings underscore Semi-PFD's practical potential, reducing the burden of fall data collection and labeling while maintaining superior performance.

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