Huimin Yan — accepted AI-conference papers

2026

LLM-Guided Diagnostic Evidence Alignment for Medical Vision–Language Pretraining under Limited Pairing

Huimin Yan, Liang Bai, Xian Yang, Long Chen

ICML 2026poster

Most existing CLIP-style medical vision--language pretraining methods rely on global or local alignment with substantial paired data. However, global alignment is easily dominated by non-diagnostic information, while local alignment fails to integrate key diagnostic evidence. As a result, learning r…

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2026

Medical Vision–Language Pretraining with LLM-Guided Temporal Supervision

Liang Bai, Zhi Wang, Huimin Yan, Xian Yang

AAAI 2026technical

Medical vision–language pretraining typically relies on static image–text pairs, overlooking temporal cues vital for understanding clinical progression. This limits model sensitivity to evolving semantics and reduces their effectiveness in real-world clinical reasoning. To address this challenge, we

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