# SelfReg-UNet: Self-Regularized UNet for Medical Image Segmentation

arXiv: https://arxiv.org/abs/2406.14896 · HTML: https://mlsanity.com/paper/2406.14896/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 30 references that are in the map.
- First posted on arXiv 21 Jun 2024.

## Builds on (most cited first, 21 of 21)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation](https://mlsanity.com/paper/2102.04306/) (2021)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2105.05537/) (2021)
- [Pruning Filters for Efficient ConvNets](https://mlsanity.com/paper/1608.08710/) (2016)
- [Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer](https://mlsanity.com/paper/1612.03928/) (2016)
- [Learning Efficient Convolutional Networks through Network Slimming](https://mlsanity.com/paper/1708.06519/) (2017)
- [Learning Structured Sparsity in Deep Neural Networks](https://mlsanity.com/paper/1608.03665/) (2016)
- [Medical Transformer: Gated Axial-Attention for Medical Image Segmentation](https://mlsanity.com/paper/2102.10662/) (2021)
- [Gland Segmentation in Colon Histology Images: The GlaS Challenge Contest](https://mlsanity.com/paper/1603.00275/) (2016)
- [UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformer](https://mlsanity.com/paper/2109.04335/) (2021)
- [Learning Lightweight Lane Detection CNNs by Self Attention Distillation](https://mlsanity.com/paper/1908.00821/) (2019)
- [Cross-Layer Distillation with Semantic Calibration](https://mlsanity.com/paper/2012.03236/) (2020)
- [HiFormer: Hierarchical Multi-scale Representations Using Transformers for Medical Image Segmentation](https://mlsanity.com/paper/2207.08518/) (2022)
- [LeViT-UNet: Make Faster Encoders with Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2107.08623/) (2021)
- [Feature-map-level Online Adversarial Knowledge Distillation](https://mlsanity.com/paper/2002.01775/) (2020)
- [Self-Regulation for Semantic Segmentation](https://mlsanity.com/paper/2108.09702/) (2021)

## Built on by (most cited first, 18 of 18)

- [MAUP: Training-free Multi-center Adaptive Uncertainty-aware Prompting for Cross-domain Few-shot Medical Image Segmentation](https://mlsanity.com/paper/2508.03511/) (2025)
- [EViT-Unet: U-Net Like Efficient Vision Transformer for Medical Image Segmentation on Mobile and Edge Devices](https://mlsanity.com/paper/2410.15036/) (2024)
- [PraNet-V2: Dual-Supervised Reverse Attention for Medical Image Segmentation](https://mlsanity.com/paper/2504.10986/) (2025)
- [Similarity Memory Prior is All You Need for Medical Image Segmentation](https://mlsanity.com/paper/2507.00585/) (2025)
- [SAGE: Shape-Adapting Gated Experts for Adaptive Histopathology Image Segmentation](https://mlsanity.com/paper/2511.18493/) (2025)
- [nnMobileNet++: Towards Efficient Hybrid Networks for Retinal Image Analysis](https://mlsanity.com/paper/2512.01273/) (2025)
- [Size and Smoothness Aware Adaptive Focal Loss for Small Tumor Segmentation](https://mlsanity.com/paper/2407.09828/) (2024)
- [KANDU-Net:A Dual-Channel U-Net with KAN for Medical Image Segmentation](https://mlsanity.com/paper/2409.20414/) (2024)
- [STA-Unet: Rethink the semantic redundant for Medical Imaging Segmentation](https://mlsanity.com/paper/2410.11578/) (2024)
- [MSV-Mamba: A Multiscale Vision Mamba Network for Echocardiography Segmentation](https://mlsanity.com/paper/2501.07120/) (2025)
- [From Spots to Pixels: Dense Spatial Gene Expression Prediction from Histology Images](https://mlsanity.com/paper/2503.01347/) (2025)
- [HER-Seg: Holistically Efficient Segmentation for High-Resolution Medical Images](https://mlsanity.com/paper/2504.06205/) (2025)
- [GNCAF: A GNN-based Neighboring Context Aggregation Framework for Tertiary Lymphoid Structures Semantic Segmentation in WSI](https://mlsanity.com/paper/2505.08430/) (2025)
- [RDTE-UNet: A Boundary and Detail Aware UNet for Precise Medical Image Segmentation](https://mlsanity.com/paper/2511.01328/) (2025)
- [GCA-ResUNet: Medical Image Segmentation Using Grouped Coordinate Attention](https://mlsanity.com/paper/2512.23990/) (2025)
- [Text as Illumination: Spatial Contrastive Retinex Learning for Language-guided Medical Image Segmentation](https://mlsanity.com/paper/2606.27794/) (2026)
- [Does Your ViT Still Need U-Net for Segmentation?](https://mlsanity.com/paper/2607.00223/) (2026)
- [AdaKAN: A dual-branch adaptive Kolmogorov-Arnold network for medical image segmentation](https://mlsanity.com/paper/2607.22891/) (2026)

## Abstract (arXiv)

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 urlhttps://github.com/ChongQingNoSubway/SelfReg-UNet