# A Weakly Supervised Consistency-based Learning Method for COVID-19 Segmentation in CT Images

arXiv: https://arxiv.org/abs/2007.02180 · HTML: https://mlsanity.com/paper/2007.02180/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jul 2020.

## Built on by (most cited first, 24 of 24)

- [Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Data](https://mlsanity.com/paper/2103.16607/) (2021)
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- [Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models](https://mlsanity.com/paper/2103.00429/) (2021)
- [BCS-Net: Boundary, Context and Semantic for Automatic COVID-19 Lung Infection Segmentation from CT Images](https://mlsanity.com/paper/2207.08114/) (2022)
- [Non-Iterative Scribble-Supervised Learning with Pacing Pseudo-Masks for Medical Image Segmentation](https://mlsanity.com/paper/2210.10956/) (2022)
- [Dense Regression Activation Maps For Lesion Segmentation in CT scans of COVID-19 patients](https://mlsanity.com/paper/2105.11748/) (2021)
- [A Deep Learning Localization Method for Measuring Abdominal Muscle Dimensions in Ultrasound Images](https://mlsanity.com/paper/2109.14919/) (2021)
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- [Annotation by Clicks: A Point-Supervised Contrastive Variance Method for Medical Semantic Segmentation](https://mlsanity.com/paper/2212.08774/) (2022)
- [DECOR-NET: A COVID-19 Lung Infection Segmentation Network Improved by Emphasizing Low-level Features and Decorrelating Features](https://mlsanity.com/paper/2302.14277/) (2023)
- [A Probabilistic Hadamard U-Net for MRI Bias Field Correction](https://mlsanity.com/paper/2403.05024/) (2024)
- [D-TrAttUnet: Dual-Decoder Transformer-Based Attention Unet Architecture for Binary and Multi-classes Covid-19 Infection Segmentation](https://mlsanity.com/paper/2303.15576/) (2023)
- [D-TrAttUnet: Toward Hybrid CNN-Transformer Architecture for Generic and Subtle Segmentation in Medical Images](https://mlsanity.com/paper/2405.04169/) (2024)
- [Data Shapley Value for Handling Noisy Labels: An application in Screening COVID-19 Pneumonia from Chest CT Scans](https://mlsanity.com/paper/2110.08726/) (2021)
- [Novel coronavirus pneumonia lesion segmentation in CT images](https://mlsanity.com/paper/2110.12827/) (2021)
- [ST-FL: Style Transfer Preprocessing in Federated Learning for COVID-19 Segmentation](https://mlsanity.com/paper/2203.13680/) (2022)
- [Weakly-Supervised Learning via Multi-Lateral Decoder Branching for Tool Segmentation in Robot-Assisted Cardiovascular Catheterization](https://mlsanity.com/paper/2404.07594/) (2024)
- [INSIGHT: Explainable Weakly-Supervised Medical Image Analysis](https://mlsanity.com/paper/2412.02012/) (2024)
- [Weakly Supervised Teacher-Student Framework with Progressive Pseudo-mask Refinement for Gland Segmentation](https://mlsanity.com/paper/2603.08605/) (2026)
- [COVID-Rate: An Automated Framework for Segmentation of COVID-19 Lesions from Chest CT Scans](https://mlsanity.com/paper/2107.01527/) (2021)
- [Decoupling Deep Learning for Interpretable Image Recognition](https://mlsanity.com/paper/2210.08336/) (2022)
- [Semi-Supervised Relational Contrastive Learning](https://mlsanity.com/paper/2304.05047/) (2023)
- [FeaInfNet: Diagnosis in Medical Image with Feature-Driven Inference and Visual Explanations](https://mlsanity.com/paper/2312.01871/) (2023)

## Abstract (arXiv)

Coronavirus Disease 2019 (COVID-19) has spread aggressively across the world causing an existential health crisis. Thus, having a system that automatically detects COVID-19 in tomography (CT) images can assist in quantifying the severity of the illness. Unfortunately, labelling chest CT scans requires significant domain expertise, time, and effort. We address these labelling challenges by only requiring point annotations, a single pixel for each infected region on a CT image. This labeling scheme allows annotators to label a pixel in a likely infected region, only taking 1-3 seconds, as opposed to 10-15 seconds to segment a region. Conventionally, segmentation models train on point-level annotations using the cross-entropy loss function on these labels. However, these models often suffer from low precision. Thus, we propose a consistency-based (CB) loss function that encourages the output predictions to be consistent with spatial transformations of the input images. The experiments on 3 open-source COVID-19 datasets show that this loss function yields significant improvement over conventional point-level loss functions and almost matches the performance of models trained with full supervision with much less human effort. Code is available at: urlhttps://github.com/IssamLaradji/covid19weaksupervision.