Hyperbolic vision language representation learning on chest radiology images.

Area of Science:

• Medical Informatics
• Computer Vision
• Natural Language Processing

Background:

• Hyperbolic embeddings are effective for visual-semantic representation learning due to their hierarchy modeling capabilities.
• Medical text data, such as radiology reports, presents unique challenges for vision-language models due to complex sentence structures.

Purpose of the Study:

• To propose a novel pretraining method for medical image-text data using hyperbolic space.
• To improve the comprehension of free-text medical reports by vision-language models.
• To enhance zero-shot learning performance in medical visual-semantic tasks.

Main Methods:

• Utilized structured radiology reports converted into sentences via prompt engineering.
• Introduced a global + local image feature extraction module to capture regional disease/symptom information.
• Employed entailment loss in hyperbolic space to model partial order relationships between images and texts.

Main Results:

• The proposed method demonstrates superior generalization capabilities.
• Achieved better performance compared to baseline methods across various zero-shot tasks and datasets.
• Effectively addressed the challenge of comprehending complex medical text.

Conclusions:

• The novel hyperbolic pretraining method enhances medical image-text representation learning.
• This approach offers significant improvements for zero-shot learning in the medical domain.
• The method shows promise for clinical applications requiring robust visual-semantic understanding.