Temporal Inversion for Learning Interval Change in Chest X-Rays
Summary: arXiv:2604.04563v2 Announce Type: replace-cross
Recent advancements in the field of vision-language pretraining have significantly enhanced the capabilities of medical foundation models. However, a critical aspect that remains unaddressed is the analysis of radiographs in isolation. This oversight is particularly detrimental in clinical settings where comparing prior and current images is essential for assessing interval change, especially in the context of chest radiographs (CXRs).
Radiologists must evaluate not only the static appearance of findings in these images but also how these findings evolve over time. In response to this need, we introduce a novel framework known as TILA (Temporal Inversion-aware Learning and Alignment). This framework employs temporal inversion—reversing image pairs—as a supervisory signal to enhance the sensitivity of existing temporal vision-language models to directional change.
Introduction to TILA Framework
TILA represents a simple yet effective approach that integrates inversion-aware objectives throughout various stages, including pretraining, fine-tuning, and inference. This methodology complements conventional appearance modeling by explicitly focusing on the learning of temporal order in medical imaging.
Key Features of TILA
- Temporal Inversion: Utilizes reversed image pairs as a supervisory signal, improving the model’s ability to recognize changes over time.
- Integration Across Stages: Ensures that inversion-aware learning is applied consistently from pretraining through to inference.
- Unified Evaluation Protocol: Proposes a standardized method to assess order sensitivity and consistency under temporal inversion.
- MS-CXR-Tretrieval: Introduces a retrieval evaluation set that can be applied to any temporal CXR dataset, enhancing comparative analysis.
Experimental Validation
We conducted experiments on multiple public datasets as well as real-world hospital cohorts to evaluate the effectiveness of the TILA framework. The results demonstrated a consistent improvement in two main areas:
- Progression Classification: TILA showed enhanced capabilities in classifying the progression of findings between images, indicating a better understanding of temporal changes.
- Temporal Embedding Alignment: There was a notable improvement in aligning temporal embeddings, showcasing the framework’s ability to accurately represent the temporal relationships of findings.
Conclusion
The introduction of TILA marks a significant advancement in the analysis of chest radiographs, offering a comprehensive solution that emphasizes the importance of temporal change in medical imaging. By integrating inversion-aware learning objectives, TILA not only enhances existing temporal vision-language models but also sets a new standard for evaluating temporal changes in radiographic findings. As the demand for more sophisticated medical imaging analysis continues to grow, frameworks like TILA will play a crucial role in bridging the gap between static image analysis and dynamic clinical assessment.