MMC-CS: Multi-branch multi-stage contrastive learning for self-supervised compressed sensing

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: 0.3-1.6 dB average PSNR improvement over existing self-supervised methods

• Primary Outcome: PSNR improvement in image reconstruction quality

• Nnt Or Sensitivity: Competitive performance with state-of-the-art supervised methods

• Confidence Interval: Not reported

Clinical Application

Requires specialized deep learning infrastructure and expertise for implementation Relevant to Australian medical imaging facilities using compressed sensing techniques, particularly in resource-constrained settings where data acquisition is challenging Medical imaging applications requiring compressed sensing, particularly where ground-truth data is limited or unavailable

Abstract

In recent years, optimization-inspired networks that integrate optimization theory into deep neural networks (DNNs) have achieved remarkable success in image compressed sensing (ICS). However, current DNN-based ICS methods face two key challenges: (1) the difficulty of acquiring ground-truth (GT) data from labeled measurements and (2) the underutilization of measurement data. To address these issues, a novel self-supervised deep learning framework is proposed to solve the inverse problem in ICS. Building upon traditional self-supervised learning, our proposed method leverages measurement values through a multi-branch, multi-stage progressive cross-contrast structure, enabling effective learning of the common prior of the original image in the absence of GT data. Based on this framework, a novel Multi-branch Multi-stage Cross-contrast CS (MMC-CS) end-to-end DNN is designed, which unfolds the Proximal Gradient Descent (PGD) algorithm. Our approach further implements multi-scale co-optimization in the image reconstruction process, integrating both the image paths (IPs) and convolutional feature paths (CFPs). Additionally, the reconstruction performance across multiple spatial levels is enhanced by incorporating wavelet convolution (WTConv). Extensive experiments demonstrate that our proposed method outperforms existing self-supervised approaches, achieving an average PSNR improvement of 0.3-1.6 dB. Moreover, it shows strong potential to compete with state-of-the-art supervised methods.

References

1. 1.Zhang, Y., Wang, H., & Xia, Y. (2026). MMC-CS: Multi-branch multi-stage contrastive learning for self-supervised compressed sensing. Neural Networks, 108475. https://doi.org/10.1016/j.neunet.2025.108475