[2604.04563] Temporal Inversion for Learning Interval Change in Chest X-Rays

Computer Science > Computer Vision and Pattern Recognition arXiv:2604.04563 (cs) [Submitted on 6 Apr 2026] Title:Temporal Inversion for Learning Interval Change in Chest X-Rays Authors:Hanbin Ko, Kyeongmin Jeon, Doowoong Choi, Chang Min Park View a PDF of the paper titled Temporal Inversion for Learning Interval Change in Chest X-Rays, by Hanbin Ko and 3 other authors View PDF Abstract:Recent advances in vision--language pretraining have enabled strong medical foundation models, yet most analyze radiographs in isolation, overlooking the key clinical task of comparing prior and current images to assess interval change. For chest radiographs (CXRs), capturing interval change is essential, as radiologists must evaluate not only the static appearance of findings but also how they evolve over time. We introduce TILA (Temporal Inversion-aware Learning and Alignment), a simple yet effective framework that uses temporal inversion, reversing image pairs, as a supervisory signal to enhance the sensitivity of existing temporal vision-language models to directional change. TILA integrates inversion-aware objectives across pretraining, fine-tuning, and inference, complementing conventional appearance modeling with explicit learning of temporal order. We also propose a unified evaluation protocol to assess order sensitivity and consistency under temporal inversion, and introduce MS-CXR-Tretrieval, a retrieval evaluation set constructed through a general protocol that can be applied to a...