# Exploiting Shared Knowledge from Non-COVID Lesions for Annotation-Efficient COVID-19 CT Lung Infection Segmentation

arXiv: https://arxiv.org/abs/2012.15564 · HTML: https://mlsanity.com/paper/2012.15564/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Dec 2020.

## Built on by (most cited first, 9 of 9)

- [Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation](https://mlsanity.com/paper/2207.14191/) (2022)
- [Uncertainty-Guided Mutual Consistency Learning for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2112.02508/) (2021)
- [SemiSAM: Enhancing Semi-Supervised Medical Image Segmentation via SAM-Assisted Consistency Regularization](https://mlsanity.com/paper/2312.06316/) (2023)
- [Dual-Task Mutual Learning for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2103.04708/) (2021)
- [Competitive Ensembling Teacher-Student Framework for Semi-Supervised Left Atrium MRI Segmentation](https://mlsanity.com/paper/2310.13955/) (2023)
- [Beyond Pixel-Wise Supervision for Medical Image Segmentation: From Traditional Models to Foundation Models](https://mlsanity.com/paper/2404.13239/) (2024)
- [Spark in the Dark: Evaluating Encoder-Decoder Pairs for COVID-19 CT's Semantic Segmentation](https://mlsanity.com/paper/2109.14818/) (2021)
- [MEDPSeg: Hierarchical polymorphic multitask learning for the segmentation of ground-glass opacities, consolidation, and pulmonary structures on computed tomography](https://mlsanity.com/paper/2312.02365/) (2023)
- [C3PS: Context-aware Conditional Cross Pseudo Supervision for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2306.08275/) (2023)

## Abstract (arXiv)

The novel Coronavirus disease (COVID-19) is a highly contagious virus and has spread all over the world, posing an extremely serious threat to all countries. Automatic lung infection segmentation from computed tomography (CT) plays an important role in the quantitative analysis of COVID-19. However, the major challenge lies in the inadequacy of annotated COVID-19 datasets. Currently, there are several public non-COVID lung lesion segmentation datasets, providing the potential for generalizing useful information to the related COVID-19 segmentation task. In this paper, we propose a novel relation-driven collaborative learning model to exploit shared knowledge from non-COVID lesions for annotation-efficient COVID-19 CT lung infection segmentation. The model consists of a general encoder to capture general lung lesion features based on multiple non-COVID lesions, and a target encoder to focus on task-specific features based on COVID-19 infections. Features extracted from the two parallel encoders are concatenated for the subsequent decoder part. We develop a collaborative learning scheme to regularize feature-level relation consistency of given input and encourage the model to learn more general and discriminative representation of COVID-19 infections. Extensive experiments demonstrate that trained with limited COVID-19 data, exploiting shared knowledge from non-COVID lesions can further improve state-of-the-art performance with up to 3.0% in dice similarity coefficient and 4.2% in normalized surface dice. Our proposed method promotes new insights into annotation-efficient deep learning for COVID-19 infection segmentation and illustrates strong potential for real-world applications in the global fight against COVID-19 in the absence of sufficient high-quality annotations.