# DCSAU-Net: A Deeper and More Compact Split-Attention U-Net for Medical Image Segmentation

arXiv: https://arxiv.org/abs/2202.00972 · HTML: https://mlsanity.com/paper/2202.00972/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 52 references that are in the map.
- First posted on arXiv 2 Feb 2022.

## Builds on (most cited first, 30 of 30)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [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)
- [MMDetection: Open MMLab Detection Toolbox and Benchmark](https://mlsanity.com/paper/1906.07155/) (2019)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Segmentation Transformer: Object-Contextual Representations for Semantic Segmentation](https://mlsanity.com/paper/1909.11065/) (2019)
- [Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)](https://mlsanity.com/paper/1710.05006/) (2017)
- [Res2Net: A New Multi-scale Backbone Architecture](https://mlsanity.com/paper/1904.01169/) (2019)
- [ResNeSt: Split-Attention Networks](https://mlsanity.com/paper/2004.08955/) (2020)
- [Road Extraction by Deep Residual U-Net](https://mlsanity.com/paper/1711.10684/) (2017)
- [UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation](https://mlsanity.com/paper/2004.08790/) (2020)
- [ResUNet++: An Advanced Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1911.07067/) (2019)
- [UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformer](https://mlsanity.com/paper/2109.04335/) (2021)
- [Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation](https://mlsanity.com/paper/1802.06955/) (2018)
- [Understanding Adversarial Attacks on Deep Learning Based Medical Image Analysis Systems](https://mlsanity.com/paper/1907.10456/) (2019)
- [DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation](https://mlsanity.com/paper/2006.04868/) (2020)
- [LeViT-UNet: Make Faster Encoders with Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2107.08623/) (2021)
- [Multi-Task Attention-Based Semi-Supervised Learning for Medical Image Segmentation](https://mlsanity.com/paper/1907.12303/) (2019)
- [FocusNet: An attention-based Fully Convolutional Network for Medical Image Segmentation](https://mlsanity.com/paper/1902.03091/) (2019)
- [X-Net: Brain Stroke Lesion Segmentation Based on Depthwise Separable Convolution and Long-range Dependencies](https://mlsanity.com/paper/1907.07000/) (2019)
- [Deeply-Supervised Density Regression for Automatic Cell Counting in Microscopy Images](https://mlsanity.com/paper/2011.03683/) (2020)
- [Targeted Gradient Descent: A Novel Method for Convolutional Neural Networks Fine-tuning and Online-learning](https://mlsanity.com/paper/2109.14729/) (2021)

## Built on by (most cited first, 30 of 37)

- [Modality-agnostic Domain Generalizable Medical Image Segmentation by Multi-Frequency in Multi-Scale Attention](https://mlsanity.com/paper/2405.06284/) (2024)
- [A Comprehensive Review of U-Net and Its Variants: Advances and Applications in Medical Image Segmentation](https://mlsanity.com/paper/2502.06895/) (2025)
- [SeUNet-Trans: A Simple yet Effective UNet-Transformer Model for Medical Image Segmentation](https://mlsanity.com/paper/2310.09998/) (2023)
- [BRAU-Net++: U-Shaped Hybrid CNN-Transformer Network for Medical Image Segmentation](https://mlsanity.com/paper/2401.00722/) (2024)
- [SA2-Net: Scale-aware Attention Network for Microscopic Image Segmentation](https://mlsanity.com/paper/2309.16661/) (2023)
- [Free Lunch for Federated Remote Sensing Target Fine-Grained Classification: A Parameter-Efficient Framework](https://mlsanity.com/paper/2401.01493/) (2024)
- [MCANet: Medical Image Segmentation with Multi-Scale Cross-Axis Attention](https://mlsanity.com/paper/2312.08866/) (2023)
- [TV-SAM: Increasing Zero-Shot Segmentation Performance on Multimodal Medical Images Using GPT-4 Generated Descriptive Prompts Without Human Annotation](https://mlsanity.com/paper/2402.15759/) (2024)
- [NuSegDG: Integration of Heterogeneous Space and Gaussian Kernel for Domain-Generalized Nuclei Segmentation](https://mlsanity.com/paper/2408.11787/) (2024)
- [MambaVesselNet++: A Hybrid CNN-Mamba Architecture for Medical Image Segmentation](https://mlsanity.com/paper/2507.19931/) (2025)
- [LSSF-Net: Lightweight Segmentation with Self-Awareness, Spatial Attention, and Focal Modulation](https://mlsanity.com/paper/2409.01572/) (2024)
- [Full-scale Representation Guided Network for Retinal Vessel Segmentation](https://mlsanity.com/paper/2501.18921/) (2025)
- [Co-Seg++: Mutual Prompt-Guided Collaborative Learning for Versatile Medical Segmentation](https://mlsanity.com/paper/2506.17159/) (2025)
- [Co-Seg: Mutual Prompt-Guided Collaborative Learning for Tissue and Nuclei Segmentation](https://mlsanity.com/paper/2509.06740/) (2025)
- [An Enhanced Harmonic Densely Connected Hybrid Transformer Network Architecture for Chronic Wound Segmentation Utilising Multi-Colour Space Tensor Merging](https://mlsanity.com/paper/2410.03359/) (2024)
- [MambaU-Lite: A Lightweight Model based on Mamba and Integrated Channel-Spatial Attention for Skin Lesion Segmentation](https://mlsanity.com/paper/2412.01405/) (2024)
- [FMS2: Unified Flow Matching for Segmentation and Synthesis of Thin Structures](https://mlsanity.com/paper/2603.13659/) (2026)
- [DCELANM-Net:Medical Image Segmentation based on Dual Channel Efficient Layer Aggregation Network with Learner](https://mlsanity.com/paper/2304.09620/) (2023)
- [MM-UNet: Morph Mamba U-shaped Convolutional Networks for Retinal Vessel Segmentation](https://mlsanity.com/paper/2511.02193/) (2025)
- [Segmentation method of U-net sheet metal engineering drawing based on CBAM attention mechanism](https://mlsanity.com/paper/2209.14102/) (2022)
- [MS-DCANet: A Novel Segmentation Network For Multi-Modality COVID-19 Medical Images](https://mlsanity.com/paper/2210.12361/) (2022)
- [DSNet: a simple yet efficient network with dual-stream attention for lesion segmentation](https://mlsanity.com/paper/2211.16950/) (2022)
- [SHARM: Segmented Head Anatomical Reference Models](https://mlsanity.com/paper/2309.06677/) (2023)
- [M3BUNet: Mobile Mean Max UNet for Pancreas Segmentation on CT-Scans](https://mlsanity.com/paper/2401.10419/) (2024)
- [DmADs-Net: Dense multiscale attention and depth-supervised network for medical image segmentation](https://mlsanity.com/paper/2405.00472/) (2024)
- [LiteNeXt: A Novel Lightweight ConvMixer-based Model with Self-embedding Representation Parallel for Medical Image Segmentation](https://mlsanity.com/paper/2405.15779/) (2024)
- [Evaluation of Blood Vessel Segmentation Methods on Hard-to-Detect Vascular Structures](https://mlsanity.com/paper/2406.13128/) (2024)
- [TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation](https://mlsanity.com/paper/2502.09931/) (2025)
- [Prompt-Guided Dual-Path UNet with Mamba for Medical Image Segmentation](https://mlsanity.com/paper/2503.19589/) (2025)
- [TCSAFormer: Efficient Vision Transformer with Token Compression and Sparse Attention for Medical Image Segmentation](https://mlsanity.com/paper/2508.04058/) (2025)

## Abstract (arXiv)

Deep learning architecture with convolutional neural network (CNN) achieves outstanding success in the field of computer vision. Where U-Net, an encoder-decoder architecture structured by CNN, makes a great breakthrough in biomedical image segmentation and has been applied in a wide range of practical scenarios. However, the equal design of every downsampling layer in the encoder part and simply stacked convolutions do not allow U-Net to extract sufficient information of features from different depths. The increasing complexity of medical images brings new challenges to the existing methods. In this paper, we propose a deeper and more compact split-attention u-shape network (DCSAU-Net), which efficiently utilises low-level and high-level semantic information based on two novel frameworks: primary feature conservation and compact split-attention block. We evaluate the proposed model on CVC-ClinicDB, 2018 Data Science Bowl, ISIC-2018 and SegPC-2021 datasets. As a result, DCSAU-Net displays better performance than other state-of-the-art (SOTA) methods in terms of the mean Intersection over Union (mIoU) and F1-socre. More significantly, the proposed model demonstrates excellent segmentation performance on challenging images. The code for our work and more technical details can be found at https://github.com/xq141839/DCSAU-Net.