ArticleIEEE transactions on medical imaging2021
CA-Net: Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation.
Article in IEEE transactions on medical imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 113 papers, 1 of them a synthesis that pooled it.
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Who cites it
113 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Roles and applications of artificial intelligence in fetal and placental MRI: a literature review.BMC pregnancy and childbirth · 2026Pooled it
- Deep Learning with Attention Mechanism for Pediatric Appendicitis Detection on Plain Abdominal Radiographs.Journal of imaging informatics in medicine · 2026Article
- Erasing and Refining Discriminative Features for CNV Subtype Classification in OCT Images.Journal of imaging · 2026Article
- Deep Learning-Assisted Quality Control of Histology Teaching Slides: Detection and Localization of Tissue Fold Artifacts in H&E-Stained Images.Bioengineering (Basel, Switzerland) · 2026Article
- MSA U-Net: a multi-scale attention-enhanced network for automatic segmentation of uterine lymph nodes in MRI images.Journal of the Egyptian National Cancer Institute · 2026Article
- Fusion attention-based nasopharyngeal carcinoma segmentation model in predicting the clinical outcome of cervical lymph node residue after IMRT.BMC medical imaging · 2026Article
- Multi-scale and edge-aware IFGNet for precise skin lesion segmentation in high-resolution dermoscopic images.Scientific reports · 2026Article
- Semi-supervised Segmentation Network Based on Prototype-Oriented Local Contrastive Learning for Pregnancy Tissue in MR Images.Journal of imaging informatics in medicine · 2026Article
- Article
- Deep Learning-Based Instance Appraisable Model (EDi Pain) for Pain Estimation via Facial Videos: A Retrospective Analysis and a Prospective Emergency Department Study.Journal of imaging informatics in medicine · 2026Article
- Attention-Driven Deep Learning for Hydrocephalus: Preliminary Benchmarking of Multiscale 3D Attention Against Conventional 3D CNN Models Using Ventricular Enlargement on CT.Journal of imaging informatics in medicine · 2026Article
- Advances and challenges in AI-assisted MRI for lumbar disc degeneration detection and classification.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Review
- LDFSAM: Localization Distillation-Enhanced Feature Prompting SAM for Medical Image Segmentation.Journal of imaging · 2026Article
- CFG-MambaNet: Contextual and Frequency-Guided Mamba Network for medical image segmentation.NPJ digital medicine · 2026Article
- From light to sound: Seeing and hearing the placenta in health and disease.Science advances · 2026Review
- DenseUNet for Breast Cancer Segmentation in Histopathological Images.Journal of medical signals and sensors · 2026Article
- Deep learning-based automated detection of fetal corpus callosum abnormalities in prenatal ultrasound.Frontiers in pediatrics · 2026Article
- Enhanced medical image segmentation using optimized bidirectional LSTM and dolphin partner optimizer.PloS one · 2026Article
- EASNet: Edge-aware Segmentation Network for Skin Lesion Segmentation with Boundary-aware and Frequency Attention Mechanisms.Interdisciplinary sciences, computational life sciences · 2025Article
- Hard exudates segmentation for retinal fundus images based on longitudinal multi-scale fusion network.Medical & biological engineering & computing · 2025Article
53 more citing papers are in PubMed but not listed here.
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9 authors.
Funding
Abstract
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.
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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.