@article{Hu2026, 
author = {Bo-Cheng Hu and Ge-Peng Ji and Dian Shao and Deng-Ping Fan},
title = {PraNet-V2: Dual-supervised reverse attention for medical image segmentation},
year = {2026},
journal = {Computational Visual Media},
volume = {12},
number = {2},
pages = {493-500},
keywords = {semantic segmentation, medical image segmentation, reverse attention (RA), dual supervision},
url = {https://www.sciopen.com/article/10.26599/CVM.2025.9450510},
doi = {10.26599/CVM.2025.9450510},
abstract = {Accurate medical image segmentation is essential for effective diagnosis and treatment. Previously we proposed PraNet-V1 as a means to enhance polyp segmentation, introducing a reverse attention (RA) module that utilizes background information. However, PraNet-V1 struggles with multi-class segmentation tasks. To address this limitation, we here propose PraNet-V2, which can effectively handle a broader range of tasks, including multi-class segmentation. At the core of PraNet-V2 is our dual-supervised reverse attention (DSRA) module, which incorporates explicit background supervision, independent background modeling, and semantically enriched attention fusion. Our PraNet-V2 framework exhibits strong performance on four polyp segmentation datasets. Moreover, the integration of DSRA into three state-of-the-art semantic segmentation models enables iterative refinement of foreground segmentation, yielding improvements of up to 1.36% in mean Dice score. Jittor code and supplementary materials are available at https://github.com/ai4colonoscopy/PraNet-V2/tree/main/binary_seg/jittor.}
}