PraNet-V2:Dual-supervised reverse attention for medical image segmentationOA

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.