Xinqiang Wang, Wenhuan Lu, Si Li, Ke Zheng, Junhai Xu, Jianguo Wei
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Semisupervised Medical Image Segmentation through Prototype-Based Mutual Consistency Learning
Medical image segmentation is a critical task in the healthcare field. While deep learning techniques have shown promise in this area, they often require a large number of accurately labeled images. To address this issue, semisupervised learning has emerged as a potential solution by reducing the reliance on precise annotations. Among these approaches, the student-teacher framework has garnered attention, but it is limited in its reliance solely on the teacher model for information. To overcome this limitation, we propose a prototype-based mutual consistency learning (PMCL) framework. This framework utilizes two branches that learn from each other, incorporating supervision loss and consistency loss to adapt to minor data perturbations and structural differences. By employing prototype consistency learning, we are able to achieve reliable consistency loss. Our experiments on three public medical image datasets demonstrate that PMCL outperforms other state-of-the-art methods, indicating its potential in semisupervised medical image segmentation. Our framework has the potential to assist medical professionals in enhancing their diagnoses and delivering improved patient care.
期刊介绍:
The International Journal of Intelligent Systems serves as a forum for individuals interested in tapping into the vast theories based on intelligent systems construction. With its peer-reviewed format, the journal explores several fascinating editorials written by today''s experts in the field. Because new developments are being introduced each day, there''s much to be learned — examination, analysis creation, information retrieval, man–computer interactions, and more. The International Journal of Intelligent Systems uses charts and illustrations to demonstrate these ground-breaking issues, and encourages readers to share their thoughts and experiences.