Learning Directional Semantic Transitions for Longitudinal Chest X-ray Analysis

2026-06-14 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision & Pattern Recognition, Medical Imaging Analysis · Depth: Expert, quick

Summary

ProTrans is a novel vision-language pretraining framework designed for longitudinal Chest X-ray (CXR) analysis, addressing limitations in current methods for capturing subtle disease progression and its directional nature. ProTrans formulates disease progression as a directional semantic transition between paired CXR studies. It utilizes radiology reports to anchor individual CXR representations within interpretable disease states. The framework introduces a learnable progression feature map to explicitly encode semantic shifts between these states, aligning them with progression descriptions derived from reports. To ensure direction-aware perception, ProTrans incorporates a reversed temporal modeling process and enforces bidirectional reconstruction consistency across states and transitions. This approach effectively disentangles directional semantics and promotes coherent trajectory modeling. Extensive experiments on downstream tasks, including disease progression classification and progression captioning, demonstrate ProTrans's consistent outperformance of existing methods, establishing a unified pretraining framework for longitudinal CXR understanding.

Key takeaway

For AI Scientists developing longitudinal medical imaging models, ProTrans offers a robust framework for capturing directional disease progression. You should consider integrating vision-language pretraining and report-derived semantic anchoring to improve model interpretability and performance. This approach enhances accuracy in tasks like progression classification and captioning, providing a unified solution for complex CXR analysis.

Key insights

ProTrans models disease progression in CXRs as directional semantic transitions using vision-language pretraining and radiology reports.

Principles

• Disease progression is inherently directional.
• Radiology reports anchor interpretable disease states.
• Bidirectional consistency enhances trajectory modeling.

Method

ProTrans formulates progression as directional semantic transitions, using radiology reports to anchor CXR states. It employs a learnable progression feature map, reversed temporal modeling, and bidirectional reconstruction consistency.

In practice

• Classify disease progression.
• Generate progression captions.
• Unify longitudinal CXR analysis.

Topics

• Chest X-ray Analysis
• Longitudinal Imaging
• Vision-Language Pretraining
• Disease Progression
• Medical Imaging AI

Code references

• RPIDIAL/ProTrans

Best for: Computer Vision Engineer, AI Scientist, Research Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.