# EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image Segmentation

arXiv: https://arxiv.org/abs/2405.06880 · HTML: https://mlsanity.com/paper/2405.06880/

## Key facts

- Cited by 64 papers in the map (data through 14 Oct 2026).
- Builds on 48 of its 60 references that are in the map.
- First posted on arXiv 11 May 2024.

## Builds on (most cited first, 30 of 48)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [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)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions](https://mlsanity.com/paper/2102.12122/) (2021)
- [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)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [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)
- [ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices](https://mlsanity.com/paper/1707.01083/) (2017)
- [PVT v2: Improved Baselines with Pyramid Vision Transformer](https://mlsanity.com/paper/2106.13797/) (2021)
- [Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)](https://mlsanity.com/paper/1902.03368/) (2019)
- [Kvasir-SEG: A Segmented Polyp Dataset](https://mlsanity.com/paper/1911.07069/) (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)
- [MetaFormer Is Actually What You Need for Vision](https://mlsanity.com/paper/2111.11418/) (2021)
- [MaxViT: Multi-Axis Vision Transformer](https://mlsanity.com/paper/2204.01697/) (2022)
- [PraNet: Parallel Reverse Attention Network for Polyp Segmentation](https://mlsanity.com/paper/2006.11392/) (2020)

## Built on by (most cited first, 30 of 64)

- [Frontiers in Intelligent Colonoscopy](https://mlsanity.com/paper/2410.17241/) (2024)
- [MK-UNet: Multi-kernel Lightweight CNN for Medical Image Segmentation](https://mlsanity.com/paper/2509.18493/) (2025)
- [MSVM-UNet: Multi-Scale Vision Mamba UNet for Medical Image Segmentation](https://mlsanity.com/paper/2408.13735/) (2024)
- [RWKV-UNet: Improving UNet with Long-Range Cooperation for Effective Medical Image Segmentation](https://mlsanity.com/paper/2501.08458/) (2025)
- [CENet: Context Enhancement Network for Medical Image Segmentation](https://mlsanity.com/paper/2505.18423/) (2025)
- [UniUltra: Interactive Parameter-Efficient SAM2 for Universal Ultrasound Segmentation](https://mlsanity.com/paper/2511.15771/) (2025)
- [PraNet-V2: Dual-Supervised Reverse Attention for Medical Image Segmentation](https://mlsanity.com/paper/2504.10986/) (2025)
- [Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention](https://mlsanity.com/paper/2506.18335/) (2025)
- [PP-SAM: Perturbed Prompts for Robust Adaptation of Segment Anything Model for Polyp Segmentation](https://mlsanity.com/paper/2405.16740/) (2024)
- [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)
- [FreqDINO: Frequency-Guided Adaptation for Generalized Boundary-Aware Ultrasound Image Segmentation](https://mlsanity.com/paper/2512.11335/) (2025)
- [MLLA-UNet: Mamba-like Linear Attention in an Efficient U-Shape Model for Medical Image Segmentation](https://mlsanity.com/paper/2410.23738/) (2024)
- [UniSegDiff: Boosting Unified Lesion Segmentation via a Staged Diffusion Model](https://mlsanity.com/paper/2507.18362/) (2025)
- [EEMS: Edge-Prompt Enhanced Medical Image Segmentation Based on Learnable Gating Mechanism](https://mlsanity.com/paper/2510.11287/) (2025)
- [From SAM to DINOv2: Towards Distilling Foundation Models to Lightweight Baselines for Generalized Polyp Segmentation](https://mlsanity.com/paper/2512.09307/) (2025)
- [Minding Fuzzy Regions: A Data-driven Alternating Learning Paradigm for Stable Lesion Segmentation](https://mlsanity.com/paper/2503.11140/) (2025)
- [PRAD: Periapical Radiograph Analysis Dataset and Benchmark Model Development](https://mlsanity.com/paper/2504.07760/) (2025)
- [Text-guided multi-stage cross-perception network for medical image segmentation](https://mlsanity.com/paper/2506.07475/) (2025)
- [Similarity Memory Prior is All You Need for Medical Image Segmentation](https://mlsanity.com/paper/2507.00585/) (2025)
- [Privacy-Preserving Semantic Segmentation from Ultra-Low-Resolution RGB Inputs](https://mlsanity.com/paper/2507.16034/) (2025)
- [MLRU++: Multiscale Lightweight Residual UNETR++ with Attention for Efficient 3D Medical Image Segmentation](https://mlsanity.com/paper/2507.16122/) (2025)
- [Small Lesions-aware Bidirectional Multimodal Multiscale Fusion Network for Lung Disease Classification](https://mlsanity.com/paper/2508.04205/) (2025)
- [CMFDNet: Cross-Mamba and Feature Discovery Network for Polyp Segmentation](https://mlsanity.com/paper/2508.17729/) (2025)
- [Advanced Brain Tumor Segmentation Using EMCAD: Efficient Multi-scale Convolutional Attention Decoding](https://mlsanity.com/paper/2509.05431/) (2025)
- [Dual Interaction Network with Cross-Image Attention for Medical Image Segmentation](https://mlsanity.com/paper/2509.05953/) (2025)
- [Progressive Uncertainty-Guided Evidential U-KAN for Trustworthy Medical Image Segmentation](https://mlsanity.com/paper/2510.08949/) (2025)
- [Seeing the Unseen: Mask-Driven Positional Encoding and Strip-Convolution Context Modeling for Cross-View Object Geo-Localization](https://mlsanity.com/paper/2510.20247/) (2025)
- [LoMix: Learnable Weighted Multi-Scale Logits Mixing for Medical Image Segmentation](https://mlsanity.com/paper/2510.22995/) (2025)
- [SurgAtt-Tracker: Online Surgical Attention Tracking via Temporal Proposal Reranking and Motion-Aware Refinement](https://mlsanity.com/paper/2602.20636/) (2026)

## Abstract (arXiv)

An efficient and effective decoding mechanism is crucial in medical image segmentation, especially in scenarios with limited computational resources. However, these decoding mechanisms usually come with high computational costs. To address this concern, we introduce EMCAD, a new efficient multi-scale convolutional attention decoder, designed to optimize both performance and computational efficiency. EMCAD leverages a unique multi-scale depth-wise convolution block, significantly enhancing feature maps through multi-scale convolutions. EMCAD also employs channel, spatial, and grouped (large-kernel) gated attention mechanisms, which are highly effective at capturing intricate spatial relationships while focusing on salient regions. By employing group and depth-wise convolution, EMCAD is very efficient and scales well (e.g., only 1.91M parameters and 0.381G FLOPs are needed when using a standard encoder). Our rigorous evaluations across 12 datasets that belong to six medical image segmentation tasks reveal that EMCAD achieves state-of-the-art (SOTA) performance with 79.4% and 80.3% reduction in #Params and #FLOPs, respectively. Moreover, EMCAD's adaptability to different encoders and versatility across segmentation tasks further establish EMCAD as a promising tool, advancing the field towards more efficient and accurate medical image analysis. Our implementation is available at https://github.com/SLDGroup/EMCAD.