Title: Understanding Interval Change in Chest Radiographs via Temporal Inversion
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: Longitudinal Medical Imaging, VLP, medical vlp, medical AI

Abstract: Recent advances in vision-language pretraining have led to the development of medical-specific models, yet most approaches analyze radiographs in isolation and overlook the essential clinical task of comparing prior and current images to assess patient progression. For chest radiographs, robust modeling of interval change between exams is critical, as clinical reports rely heavily on identifying whether findings have improved, worsened, or remained stable. To address this gap, we present TILA (Temporal Inversion-aware Learning and Alignment), a framework that incorporates temporal inversion as a supervisory signal. TILA enforces directional awareness between paired images when their temporal order is reversed, enabling the model to adapt predictions to temporal context while maintaining logical consistency. This inversion-aware alignment is integrated across pretraining, fine-tuning, and inference, offering a complete pipeline for paired CXRs. We also propose a new evaluation protocol to assess model sensitivity, consistency, and robustness under temporal inversion, and introduce the MS-CXR-T_retrieval benchmark for progression-aware retrieval. Experiments on public datasets and real-world hospital records demonstrate that TILA achieves state-of-the-art performance in progression classification, temporal embedding matching, and progression retrieval. These results highlight TILA’s potential to advance clinical decision-making through more reliable temporal reasoning and generalizable predictions.

TL;DR: TILA is a unified framework for chest X-ray analysis that uses temporal inversion to robustly model disease progression, achieving state-of-the-art performance in progression detection and retrieval across multiple datasets.