ArticleIEEE transactions on medical imaging2020
A Noise-Robust Framework for Automatic Segmentation of COVID-19 Pneumonia Lesions From CT Images.
Article in IEEE transactions on medical imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 131 papers, 2 of them syntheses that pooled it.
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131 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Supervised and weakly supervised deep learning models for COVID-19 CT diagnosis: A systematic review.Computer methods and programs in biomedicine · 2022Pooled it
- Application of artificial intelligence in diagnosing COVID-19 disease symptoms on chest X-rays: A systematic review.International journal of medical sciences · 2022Pooled it
- Multi-scale deformable attention fusion network with global context modeling for chest X-ray lesion segmentation.BMC medical imaging · 2026Article
- Application of transformer models in medical image segmentation: a narrative review.Quantitative imaging in medicine and surgery · 2026Review
- HFSUNet: A UNet architecture optimized for precise segmentation of pneumonia lesion boundaries.iScience · 2026Article
- EvoThy-Net: an evolutionary encoder-decoder network for thyroid nodule segmentation in ultrasound imaging.Scientific reports · 2026Article
- Automatic recognition of adrenal incidentalomas using a two-stage cascade network: a multicenter study.Annals of medicine · 2025Article
- DECE-Net: a dual-path encoder network with contour enhancement for pneumonia lesion segmentation.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- PARADISE: Personalized and regional adaptation for HIE disease identification and segmentation.Medical image analysis · 2025Article
- Liver segmentation network based on detail enhancement and multi-scale feature fusion.Scientific reports · 2025Article
- CDSE-UNet: Enhancing COVID-19 CT Image Segmentation With Canny Edge Detection and Dual-Path SENet Feature Fusion.International journal of biomedical imaging · 2025Article
- Comparing Different Data Partitioning Strategies for Segmenting Areas Affected by COVID-19 in CT Scans.Diagnostics (Basel, Switzerland) · 2024Article
- Attention-based multi-residual network for lung segmentation in diseased lungs with custom data augmentation.Scientific reports · 2024Article
- Current Diagnostic Techniques for Pneumonia: A Scoping Review.Sensors (Basel, Switzerland) · 2024Article
- [Identifying Novel Coronavirus Pneumonia With CT Images: A Deep Learning Approach With Detail Upsampling and Attention Guidance].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2024Article
- Cascaded cross-attention transformers and convolutional neural networks for multi-organ segmentation in male pelvic computed tomography.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- COVID-19 Infection Percentage Estimation from Computed Tomography Scans: Results and Insights from the International Per-COVID-19 Challenge.Sensors (Basel, Switzerland) · 2024Review
- Artificial intelligence for segmentation and classification of lobar, lobular, and interstitial pneumonia using case-specific CT information.Quantitative imaging in medicine and surgery · 2024Article
- SenseCare: a research platform for medical image informatics and interactive 3D visualization.Frontiers in radiology · 2024Article
- Development and validation of a CT-based nomogram for accurate hepatocellular carcinoma detection in high risk patients.Frontiers in oncology · 2024Article
71 more citing papers are in PubMed but not listed here.
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10 authors.
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Abstract
Segmentation of pneumonia lesions from CT scans of COVID-19 patients is important for accurate diagnosis and follow-up. Deep learning has a potential to automate this task but requires a large set of high-quality annotations that are difficult to collect. Learning from noisy training labels that are easier to obtain has a potential to alleviate this problem. To this end, we propose a novel noise-robust framework to learn from noisy labels for the segmentation task. We first introduce a noise-robust Dice loss that is a generalization of Dice loss for segmentation and Mean Absolute Error (MAE) loss for robustness against noise, then propose a novel COVID-19 Pneumonia Lesion segmentation network (COPLE-Net) to better deal with the lesions with various scales and appearances. The noise-robust Dice loss and COPLE-Net are combined with an adaptive self-ensembling framework for training, where an Exponential Moving Average (EMA) of a student model is used as a teacher model that is adaptively updated by suppressing the contribution of the student to EMA when the student has a large training loss. The student model is also adaptive by learning from the teacher only when the teacher outperforms the student. Experimental results showed that: (1) our noise-robust Dice loss outperforms existing noise-robust loss functions, (2) the proposed COPLE-Net achieves higher performance than state-of-the-art image segmentation networks, and (3) our framework with adaptive self-ensembling significantly outperforms a standard training process and surpasses other noise-robust training approaches in the scenario of learning from noisy labels for COVID-19 pneumonia lesion segmentation.
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