Semi-Supervised Medical Hyperspectral Image Segmentation Using Adversarial Consistency Constraint Learning and Cross Indication Network

Abstract

Hyperspectral imaging technology is considered a new paradigm for high-precision pathological image segmentation due to its ability to obtain spatial and spectral information of the detected object simultaneously. However, due to the time-consuming and laborious manual annotation, precise annotation of medical hyperspectral images is difficult to obtain. Therefore, there is an urgent need for a semi-supervised learning framework that can fully utilize unlabeled data for medical hyperspectral image segmentation. In this work, we propose an adversarial consistency constraint learning cross indication network (ACCL-CINet), which achieves accurate pathological image segmentation through adversarial consistency constraint learning training strategies. The ACCL-CINet comprises a contextual and structural encoder to form the spatial-spectral feature encoding part. The contextual and structural indications are aggregated into features through a cross indication attention module and finally decoded by a pixel decoder to generate prediction results. For the semi-supervised training strategy, a pixel perceptual consistency module encourages the two models to generate consistent and low-entropy predictions. Secondly, a pixel maximum neighborhood probability adversarial constraint strategy is designed, which produces high-quality pseudo labels for cross supervision training. The proposed ACCL-CINet has been rigorously evaluated on both public and private datasets, with experimental results demonstrating that it outperforms state-of-the-art semi-supervised methods. The code is available at: https://github.com/Qugeryolo/ACCL-CINet