Cross-Task Consistency-Guided Mean Teacher with Cross Pseudo Supervision for Semi-Supervised Polyp Segmentation
Main Article Content
Abstract
With the recent improvements in deep learning techniques, many polyp segmentation methods have become increasingly accurate, facilitating prompt diagnosis and treatment of colorectal cancer. However, the scarcity of labeled datasets poses significant challenges for training and developing supervised learning methods, as annotating data is a time-consuming and labor-intensive task. To address this problem, we propose CTC-MT, a semi-supervised framework for colorectal polyp segmentation based on Mean Teacher and Cross Pseudo Supervision, comprising a teacher and two students to utilize both limited labeled data and unlabeled data. With the proposed framework, we aim to encourage the similarity between the predictions from each student and the pseudo-label from the teacher for unlabeled input, and ensure the consistency between the predictions of the two students for the same input image. Additionally, we improve PMFNet architecture as the backbone model by leveraging a spatial MoE block and adding a regression branch to learn boundary features through a signed distance map and cross-task consistency. The experimental results show that our framework achieves promising performance on Kvasir-SEG, ETIS, CVC-300, CVC-ClinicDB, and CVC-ColonDB datasets compared to other state-of-the-art approaches.
Keywords
Cross Pseudo Supervision, Mean Teacher, Mixture of Experts, Polyp Segmentation, Semi-supervised Learning
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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