ArticleMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention2024
SelfReg-UNet: Self-Regularized UNet for Medical Image Segmentation.
Article in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- LQUnet: a vascular segmentation network based on multi-scale feature fusion and hierarchical self distillation.Scientific reports · 2026Article
- Efficient Medical Image Segmentation in Multisensor Imaging: A Survey in the Era of Mamba and Foundation Models.Sensors (Basel, Switzerland) · 2026Review
- CSWin-MDKDNet: cross-shaped window network with multi-dimensional fusion and knowledge distillation for medical image segmentation.Scientific reports · 2026Article
- GlomNet: glomeruli segmentation network for WSI with diverse staining protocols for kidney disease diagnosis.Frontiers in medicine · 2026Article
- TransCat: a hybrid CNN-transformer network with KAN for medical image segmentation.Frontiers in bioinformatics · 2026Article
- SCEAF-UNet: Medical image segmentation based on spatial-channel feature enhancement and adaptive fusion.PloS one · 2026Article
- Cross-modal dual-domain bi-direction feature interaction network for medical imaging semantic segmentation.Scientific reports · 2025Article
- RepSAU-Net: Semantic Segmentation of Barcodes in Complex Backgrounds via Fused Self-Attention and Reparameterization Methods.Journal of imaging · 2025Article
- A dual attention and cross layer fusion network with a hybrid CNN and transformer architecture for medical image segmentation.Scientific reports · 2025Article
- EVIT-UNET: U-NET LIKE EFFICIENT VISION TRANSFORMER FOR MEDICAL IMAGE SEGMENTATION ON MOBILE AND EDGE DEVICES.Proceedings. IEEE International Symposium on Biomedical Imaging · 2025Article
- MWG-UNet++: Hybrid Transformer U-Net Model for Brain Tumor Segmentation in MRI Scans.Bioengineering (Basel, Switzerland) · 2025Article
- Article
- Machine vision-based detection of browning maturity in shiitake cultivation sticks.Frontiers in plant science · 2025Article
- Universal Vessel Segmentation for Multi-Modality Retinal Images.IEEE transactions on image processing : a publication of the IEEE Signal Processing Society · 2025Article
- MDA-TransUNet: A Deep Learning-Based Automatic Segmentation Method for Cervical Cancer Brachytherapy.Technology in cancer research & treatmentArticle
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Since its introduction, UNet has been leading a variety of medical image segmentation tasks. Although numerous follow-up studies have also been dedicated to improving the performance of standard UNet, few have conducted in-depth analyses of the underlying interest pattern of UNet in medical image segmentation. In this paper, we explore the patterns learned in a UNet and observe two important factors that potentially affect its performance: (i) irrelative feature learned caused by asymmetric supervision; (ii) feature redundancy in the feature map. To this end, we propose to balance the supervision between encoder and decoder and reduce the redundant information in the UNet. Specifically, we use the feature map that contains the most semantic information (i.e., the last layer of the decoder) to provide additional supervision to other blocks to provide additional supervision and reduce feature redundancy by leveraging feature distillation. The proposed method can be easily integrated into existing UNet architecture in a plug-and-play fashion with negligible computational cost. The experimental results suggest that the proposed method consistently improves the performance of standard UNets on four medical image segmentation datasets. The code is available at https://github.com/ChongQingNoSubway/SelfReg-UNet.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.