ArticleIEEE transactions on medical imaging2021
Label-Free Segmentation of COVID-19 Lesions in Lung CT.
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 43 papers, 1 of them a synthesis that pooled it.
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43 citing papers in PubMed, 1 synthesis or guideline pooled it, 145 citations in OpenAlex.
- Automated COVID-19 diagnosis and prognosis with medical imaging and who is publishing: a systematic review.Physical and engineering sciences in medicine · 2022Pooled it
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- DTCR-U-Net: a dual-task consistency and multi-scale adaptive attention framework for pneumonia lesion segmentation.Medical & biological engineering & computing · 2026Article
- A self-supervised framework for emphysema anomaly detection and staging in computed tomography scans.Patterns (New York, N.Y.) · 2026Article
- Large-scale generative tumor synthesis in computed tomography images for improving tumor recognition.Nature communications · 2025Article
- Deep learning-based lung cancer risk assessment using chest computed tomography images without pulmonary nodules ≥8 mm.Translational lung cancer research · 2025Article
- An AI-Based Low-Risk Lung Health Image Visualization Framework Using LR-ULDCT.Journal of imaging informatics in medicine · 2024Article
- Segmenting and classifying lung diseases with M-Segnet and Hybrid Squeezenet-CNN architecture on CT images.PloS one · 2024Article
- Semi-supervised COVID-19 volumetric pulmonary lesion estimation on CT images using probabilistic active contour and CNN segmentation.Biomedical signal processing and control · 2023Article
- Dense regression activation maps for lesion segmentation in CT scans of COVID-19 patients.Medical image analysis · 2023Article
- Bilateral adaptive graph convolutional network on CT based Covid-19 diagnosis with uncertainty-aware consensus-assisted multiple instance learning.Medical image analysis · 2023Article
- Class-Aware Adversarial Transformers for Medical Image Segmentation.Advances in neural information processing systems · 2022Article
- Lesion segmentation in lung CT scans using unsupervised adversarial learning.Medical & biological engineering & computing · 2022Article
- Self-supervised region-aware segmentation of COVID-19 CT images using 3D GAN and contrastive learning.Computers in biology and medicine · 2022Article
- Machine Learning for Lung Cancer Diagnosis, Treatment, and Prognosis.Genomics, proteomics & bioinformatics · 2022Review
- Unsupervised segmentation and quantification of COVID-19 lesions on computed Tomography scans using CycleGAN.Methods (San Diego, Calif.) · 2022Article
- Automated Lung Segmentation from Computed Tomography Images of Normal and COVID-19 Pneumonia Patients.Iranian journal of medical sciences · 2022Article
- Incremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI Segmentation.Distributed, collaborative, and federated learning, and affordable AI and healthcare for resource diverse global health : Third MICCAI Workshop, DeCaF 2022 and Second MICCAI Workshop, FAIR 2022, held in conjunction with MICCAI 2022, Sin... · 2022Article
- FCF: Feature complement fusion network for detecting COVID-19 through CT scan images.Applied soft computing · 2022Article
- Label-free coronavirus disease 2019 lesion segmentation based on synthetic healthy lung image subtraction.Medical physics · 2022Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Scarcity of annotated images hampers the building of automated solution for reliable COVID-19 diagnosis and evaluation from CT. To alleviate the burden of data annotation, we herein present a label-free approach for segmenting COVID-19 lesions in CT via voxel-level anomaly modeling that mines out the relevant knowledge from normal CT lung scans. Our modeling is inspired by the observation that the parts of tracheae and vessels, which lay in the high-intensity range where lesions belong to, exhibit strong patterns. To facilitate the learning of such patterns at a voxel level, we synthesize 'lesions' using a set of simple operations and insert the synthesized 'lesions' into normal CT lung scans to form training pairs, from which we learn a normalcy-recognizing network (NormNet) that recognizes normal tissues and separate them from possible COVID-19 lesions. Our experiments on three different public datasets validate the effectiveness of NormNet, which conspicuously outperforms a variety of unsupervised anomaly detection (UAD) methods.
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