Enhanced gallbladder cancer detection via active and self-supervised learning integration: Innovating B-ultrasound image analysis

• Published in: PloS one Vol. 20; no. 9; p. e0330781
• Main Authors: Li, Jia, Zhou, Yu-Qian
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 16.09.2025; Public Library of Science (PLoS)
• Subjects: Accuracy; Algorithms; Annotations; Cancer; Classification; Clustering; Curricula; Data correlation; Datasets; Decision support systems; Deep Learning; Diagnosis; Efficiency; Feature extraction; Gallbladder; Gallbladder - diagnostic imaging; Gallbladder cancer; Gallbladder diseases; Gallbladder Neoplasms - diagnostic imaging; Humans; Image analysis; Image Interpretation, Computer-Assisted - methods; Image processing; Image Processing, Computer-Assisted - methods; Labeling; Machine learning; Malignancy; Medical imaging; Medical imaging equipment; Medical prognosis; Medical research; Methods; Neural networks; Prognosis; Self-supervised learning; Stability; Supervised Machine Learning; Thyroid gland; Tumors; Ultrasonic imaging; Ultrasonography - methods; Ultrasound; Ultrasound imaging
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0330781

Gallbladder cancer, a common yet often under diagnosed malignancy, is typically characterized by late detection and a poor prognosis. The rise of deep learning has introduced new methods for its early identification through B-ultrasound imaging, but there are still challenges of inefficient data labeling and feature extraction. This paper introduces a novel classification algorithm, ASGBC, intended to tackle related challenges in diagnosing gallbladder cancer using B-ultrasound images. Firstly, we combine active learning with self-supervised learning to decrease the reliance on labeled data. Secondly, we introduce the MsHop module, which effectively captures the fine textures and patterns in ultrasound images through the integration of multi-scale and high-order information, thereby improving diagnostic accuracy. Additionally, we develop a dual-branch loss function that leverages data correlation and clustering features to enhance feature extraction and model stability. The experiments on a gallbladder ultrasound dataset have confirmed the effectiveness of our algorithm, achieving an accuracy of 0.884, a specificity of 0.932, and a sensitivity of 0.912—outperforming existing methods. The results exhibit lower variance, indicating improved model stability. Furthermore, the findings demonstrate that using active learning, one can achieve comparable results to those from the full dataset with only 35% of the data, reducing annotation costs and increasing model learning efficiency. Further research will concentrate on refining the algorithm for wider clinical use and identifying additional features that may further improve diagnostic accuracy.