# CA-Net: Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation

arXiv: https://arxiv.org/abs/2009.10549 · HTML: https://mlsanity.com/paper/2009.10549/

## Key facts

- Cited by 79 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Sep 2020.

## Built on by (most cited first, 30 of 79)

- [Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective](https://mlsanity.com/paper/2302.01735/) (2023)
- [Beyond Adapting SAM: Towards End-to-End Ultrasound Image Segmentation via Auto Prompting](https://mlsanity.com/paper/2309.06824/) (2023)
- [Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives](https://mlsanity.com/paper/2206.01136/) (2022)
- [MIS-FM: 3D Medical Image Segmentation using Foundation Models Pretrained on a Large-Scale Unannotated Dataset](https://mlsanity.com/paper/2306.16925/) (2023)
- [A Survey on Deep Learning for Skin Lesion Segmentation](https://mlsanity.com/paper/2206.00356/) (2022)
- [ExAID: A Multimodal Explanation Framework for Computer-Aided Diagnosis of Skin Lesions](https://mlsanity.com/paper/2201.01249/) (2022)
- [Contrastive Semi-supervised Learning for Domain Adaptive Segmentation Across Similar Anatomical Structures](https://mlsanity.com/paper/2208.08605/) (2022)
- [CDDSA: Contrastive Domain Disentanglement and Style Augmentation for Generalizable Medical Image Segmentation](https://mlsanity.com/paper/2211.12081/) (2022)
- [MICA: Towards Explainable Skin Lesion Diagnosis via Multi-Level Image-Concept Alignment](https://mlsanity.com/paper/2401.08527/) (2024)
- [Self-eXplainable AI for Medical Image Analysis: A Survey and New Outlooks](https://mlsanity.com/paper/2410.02331/) (2024)
- [Real-time Instance Segmentation of Surgical Instruments using Attention and Multi-scale Feature Fusion](https://mlsanity.com/paper/2111.04911/) (2021)
- [Domain Composition and Attention for Unseen-Domain Generalizable Medical Image Segmentation](https://mlsanity.com/paper/2109.08852/) (2021)
- [1M parameters are enough? A lightweight CNN-based model for medical image segmentation](https://mlsanity.com/paper/2306.16103/) (2023)
- [BATFormer: Towards Boundary-Aware Lightweight Transformer for Efficient Medical Image Segmentation](https://mlsanity.com/paper/2206.14409/) (2022)
- [PyMIC: A deep learning toolkit for annotation-efficient medical image segmentation](https://mlsanity.com/paper/2208.09350/) (2022)
- [HiDiff: Hybrid Diffusion Framework for Medical Image Segmentation](https://mlsanity.com/paper/2407.03548/) (2024)
- [Clustering Propagation for Universal Medical Image Segmentation](https://mlsanity.com/paper/2403.16646/) (2024)
- [CC-SAM: SAM with Cross-feature Attention and Context for Ultrasound Image Segmentation](https://mlsanity.com/paper/2408.00181/) (2024)
- [Explainable AI (XAI) in Image Segmentation in Medicine, Industry, and Beyond: A Survey](https://mlsanity.com/paper/2405.01636/) (2024)
- [The Lighter The Better: Rethinking Transformers in Medical Image Segmentation Through Adaptive Pruning](https://mlsanity.com/paper/2206.14413/) (2022)
- [Segmentation Ability Map: Interpret deep features for medical image segmentation](https://mlsanity.com/paper/2212.09206/) (2022)
- [Attention Mechanisms in Medical Image Segmentation: A Survey](https://mlsanity.com/paper/2305.17937/) (2023)
- [Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicine](https://mlsanity.com/paper/2211.09317/) (2022)
- [MaxViT-UNet: Multi-Axis Attention for Medical Image Segmentation](https://mlsanity.com/paper/2305.08396/) (2023)
- [SSL-CPCD: Self-supervised learning with composite pretext-class discrimination for improved generalisability in endoscopic image analysis](https://mlsanity.com/paper/2306.00197/) (2023)
- [XBound-Former: Toward Cross-scale Boundary Modeling in Transformers](https://mlsanity.com/paper/2206.00806/) (2022)
- [PMFSNet: Polarized Multi-scale Feature Self-attention Network For Lightweight Medical Image Segmentation](https://mlsanity.com/paper/2401.07579/) (2024)
- [Efficient and Accurate Pneumonia Detection Using a Novel Multi-Scale Transformer Approach](https://mlsanity.com/paper/2408.04290/) (2024)
- [MCANet: Medical Image Segmentation with Multi-Scale Cross-Axis Attention](https://mlsanity.com/paper/2312.08866/) (2023)
- [SAMCT: Segment Any CT Allowing Labor-Free Task-Indicator Prompts](https://mlsanity.com/paper/2403.13258/) (2024)

## Abstract (arXiv)

Accurate medical image segmentation is essential for diagnosis and treatment planning of diseases. Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance for automatic medical image segmentation. However, they are still challenged by complicated conditions where the segmentation target has large variations of position, shape and scale, and existing CNNs have a poor explainability that limits their application to clinical decisions. In this work, we make extensive use of multiple attentions in a CNN architecture and propose a comprehensive attention-based CNN (CA-Net) for more accurate and explainable medical image segmentation that is aware of the most important spatial positions, channels and scales at the same time. In particular, we first propose a joint spatial attention module to make the network focus more on the foreground region. Then, a novel channel attention module is proposed to adaptively recalibrate channel-wise feature responses and highlight the most relevant feature channels. Also, we propose a scale attention module implicitly emphasizing the most salient feature maps among multiple scales so that the CNN is adaptive to the size of an object. Extensive experiments on skin lesion segmentation from ISIC 2018 and multi-class segmentation of fetal MRI found that our proposed CA-Net significantly improved the average segmentation Dice score from 87.77% to 92.08% for skin lesion, 84.79% to 87.08% for the placenta and 93.20% to 95.88% for the fetal brain respectively compared with U-Net. It reduced the model size to around 15 times smaller with close or even better accuracy compared with state-of-the-art DeepLabv3+. In addition, it has a much higher explainability than existing networks by visualizing the attention weight maps. Our code is available at https://github.com/HiLab-git/CA-Net