# A Deep Ensemble Learning Approach to Lung CT Segmentation for COVID-19 Severity Assessment

arXiv: https://arxiv.org/abs/2207.02322 · HTML: https://mlsanity.com/paper/2207.02322/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 17 references that are in the map.
- First posted on arXiv 5 Jul 2022.

## Builds on (most cited first, 3 of 3)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images](https://mlsanity.com/paper/2004.14133/) (2020)
- [JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation](https://mlsanity.com/paper/2004.07054/) (2020)

## Built on by (most cited first, 2 of 2)

- [Collective Intelligent Strategy for Improved Segmentation of COVID-19 from CT](https://mlsanity.com/paper/2212.12264/) (2022)
- [Detection of adrenal anomalous findings in spinal CT images using multi model graph aggregation](https://mlsanity.com/paper/2410.20568/) (2024)

## Abstract (arXiv)

We present a novel deep learning approach to categorical segmentation of lung CTs of COVID-19 patients. Specifically, we partition the scans into healthy lung tissues, non-lung regions, and two different, yet visually similar, pathological lung tissues, namely, ground-glass opacity and consolidation. This is accomplished via a unique, end-to-end hierarchical network architecture and ensemble learning, which contribute to the segmentation and provide a measure for segmentation uncertainty. The proposed framework achieves competitive results and outstanding generalization capabilities for three COVID-19 datasets. Our method is ranked second in a public Kaggle competition for COVID-19 CT images segmentation. Moreover, segmentation uncertainty regions are shown to correspond to the disagreements between the manual annotations of two different radiologists. Finally, preliminary promising correspondence results are shown for our private dataset when comparing the patients' COVID-19 severity scores (based on clinical measures), and the segmented lung pathologies. Code and data are available at our repository: https://github.com/talbenha/covid-seg