A generative model uses healthy and diseased image pairs for pixel-level chest X-ray pathology localization.
Authors
Affiliations (5)
Affiliations (5)
- Department of Automation, Tsinghua University, Beijing, China.
- Chinese PLA General Hospital, Beijing, China. [email protected].
- Department of Automation, Tsinghua University, Beijing, China. [email protected].
- Institute of Brain and Cognitive Sciences, Tsinghua University, Beijing, China. [email protected].
- Shanghai Artificial Intelligence Laboratory, Shanghai, China. [email protected].
Abstract
Medical artificial intelligence (AI) offers potential for automatic pathological interpretation, but a practicable AI model demands both pixel-level accuracy and high explainability for diagnosis. The construction of such models relies on substantial training data with fine-grained labelling, which is impractical in real applications. To circumvent this barrier, we propose a prompt-driven constrained generative model to produce anatomically aligned healthy and diseased image pairs and learn a pathology localization model in a supervised manner. This paradigm provides high-fidelity labelled data and addresses the lack of chest X-ray images with labelling at fine scales. Benefitting from the emerging text-driven generative model and the incorporated constraint, our model presents promising localization accuracy of subtle pathologies, high explainability for clinical decisions, and good transferability to many unseen pathological categories such as new prompts and mixed pathologies. These advantageous features establish our model as a promising solution to assist chest X-ray analysis. In addition, the proposed approach is also inspiring for other tasks lacking massive training data and time-consuming manual labelling.