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MedLP-HAFB-CLIP: Hierarchical Adaptive Large Model With Learnable Medical Prompts for Level II Ultrasound Standard
Jiaxin Cai1, Chenquan Dai2, Runqing Xiong3
1School of Mathematics and Statistics, Xiamen University of Technology, Xiamen, China.
Objective:
Level II ultrasound standard section classification is of great importance for accurate prenatal diagnosis. However, sonographers face challenges in distinguishing the lateral ventricle transverse section from the thalamus transverse section.
Methods:
In this study, the standard section images of second-trimester fetal level II prenatal ultrasound examinations from 1261 pregnant women are collected, and a section classification model called Medical Learnable Prompt-Hierarchical Adaptive Feature Block Contrastive Language-Image Pre-training (MedLP-HAFB-CLIP) is proposed. The model uses a meticulously designed prompt learner to generate prompts that are highly compatible with anatomical categories, effectively integrating medical knowledge. By combining fine-grained anatomical descriptions and a customized loss function, the model's discriminative ability for complex image features is significantly enhanced. Additionally, the HAFB module is proposed for multi-scale feature extraction and adaptive fusion, further improving the model's classification performance.
Results:
Experimental results demonstrate that MedLP-HAFB-CLIP significantly outperforms baseline models. It exhibits excellent performance in distinguishing the lateral ventricle transverse section and the thalamus transverse section. Moreover, in a small exploratory observer study involving 50 selected images and 4 sonographers, the model correctly classified all selected cases and showed shorter reading time than unaided interpretation; however, these preliminary findings should be interpreted with caution and require validation in larger reader studies. The codes are available at https://github.com/Chenan7/MedLP-HAFB-CLIP.git.
Conclusion:
This study provides a robust and clinically valuable tool for automating the classification of fetal ultrasound standard planes. The proposed method holds significant promise for enhancing the standardization and reliability of prenatal ultrasound screening in clinical practice.