TILA uses temporal inversion of image pairs as a supervisory signal to make existing temporal vision-language models more sensitive to directional interval changes in chest X-rays.
Making the most of text semantics to improve biomedical vision–language processing
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HTSC-CIF applies hierarchical task decomposition and cross-modal causal intervention to generate medical reports from images while addressing domain knowledge, alignment, and bias challenges.
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Temporal Inversion for Learning Interval Change in Chest X-Rays
TILA uses temporal inversion of image pairs as a supervisory signal to make existing temporal vision-language models more sensitive to directional interval changes in chest X-rays.
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Medical Report Generation: A Hierarchical Task Structure-Based Cross-Modal Causal Intervention Framework
HTSC-CIF applies hierarchical task decomposition and cross-modal causal intervention to generate medical reports from images while addressing domain knowledge, alignment, and bias challenges.