# Label-Free Segmentation of COVID-19 Lesions in Lung CT

arXiv: https://arxiv.org/abs/2009.06456 · HTML: https://mlsanity.com/paper/2009.06456/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Sep 2020.

## Built on by (most cited first, 30 of 37)

- [Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation](https://mlsanity.com/paper/2207.14191/) (2022)
- [AA-CLIP: Enhancing Zero-shot Anomaly Detection via Anomaly-Aware CLIP](https://mlsanity.com/paper/2503.06661/) (2025)
- [Towards Generalizable Tumor Synthesis](https://mlsanity.com/paper/2402.19470/) (2024)
- [Label-Free Liver Tumor Segmentation](https://mlsanity.com/paper/2303.14869/) (2023)
- [Medical World Model: Generative Simulation of Tumor Evolution for Treatment Planning](https://mlsanity.com/paper/2506.02327/) (2025)
- [Class-Aware Adversarial Transformers for Medical Image Segmentation](https://mlsanity.com/paper/2201.10737/) (2022)
- [Incremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI Segmentation](https://mlsanity.com/paper/2206.01369/) (2022)
- [From Pixel to Cancer: Cellular Automata in Computed Tomography](https://mlsanity.com/paper/2403.06459/) (2024)
- [Early Detection and Localization of Pancreatic Cancer by Label-Free Tumor Synthesis](https://mlsanity.com/paper/2308.03008/) (2023)
- [Which images to label for few-shot medical landmark detection?](https://mlsanity.com/paper/2112.04386/) (2021)
- [LeFusion: Controllable Pathology Synthesis via Lesion-Focused Diffusion Models](https://mlsanity.com/paper/2403.14066/) (2024)
- [FreeTumor: Advance Tumor Segmentation via Large-Scale Tumor Synthesis](https://mlsanity.com/paper/2406.01264/) (2024)
- [Synthetic Tumors Make AI Segment Tumors Better](https://mlsanity.com/paper/2210.14845/) (2022)
- [A Survey of Deep Learning Techniques for the Analysis of COVID-19 and their usability for Detecting Omicron](https://mlsanity.com/paper/2202.06372/) (2022)
- [Promoting Saliency From Depth: Deep Unsupervised RGB-D Saliency Detection](https://mlsanity.com/paper/2205.07179/) (2022)
- [Analyzing Tumors by Synthesis](https://mlsanity.com/paper/2409.06035/) (2024)
- [Synthetic Data as Validation](https://mlsanity.com/paper/2310.16052/) (2023)
- [Towards Accurate Unified Anomaly Segmentation](https://mlsanity.com/paper/2501.12295/) (2025)
- [Dense Regression Activation Maps For Lesion Segmentation in CT scans of COVID-19 patients](https://mlsanity.com/paper/2105.11748/) (2021)
- [Towards multi-modal anatomical landmark detection for ultrasound-guided brain tumor resection with contrastive learning](https://mlsanity.com/paper/2307.14523/) (2023)
- [LesionDiffusion: Towards Text-controlled General Lesion Synthesis](https://mlsanity.com/paper/2503.00741/) (2025)
- [Preserving Tumor Volumes for Unsupervised Medical Image Registration](https://mlsanity.com/paper/2309.10153/) (2023)
- [Adversarial Medical Image with Hierarchical Feature Hiding](https://mlsanity.com/paper/2312.01679/) (2023)
- [STS MICCAI 2023 Challenge: Grand challenge on 2D and 3D semi-supervised tooth segmentation](https://mlsanity.com/paper/2407.13246/) (2024)
- [Segmentation of Lungs COVID Infected Regions by Attention Mechanism and Synthetic Data](https://mlsanity.com/paper/2108.08895/) (2021)
- [Novel coronavirus pneumonia lesion segmentation in CT images](https://mlsanity.com/paper/2110.12827/) (2021)
- [Reliability of deep learning models for anatomical landmark detection: The role of inter-rater variability](https://mlsanity.com/paper/2411.17850/) (2024)
- [COVID-Rate: An Automated Framework for Segmentation of COVID-19 Lesions from Chest CT Scans](https://mlsanity.com/paper/2107.01527/) (2021)
- [Unsupervised COVID-19 Lesion Segmentation in CT Using Cycle Consistent Generative Adversarial Network](https://mlsanity.com/paper/2111.11602/) (2021)
- [A novel unsupervised covid lung lesion segmentation based on the lung tissue identification](https://mlsanity.com/paper/2202.12148/) (2022)

## Abstract (arXiv)

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 pixel-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 pixel level, we synthesize `lesions' using a set of surprisingly simple operations and insert the synthesized `lesions' into normal CT lung scans to form training pairs, from which we learn a normalcy-converting network (NormNet) that turns an 'abnormal' image back to normal. Our experiments on three different datasets validate the effectiveness of NormNet, which conspicuously outperforms a variety of unsupervised anomaly detection (UAD) methods.