# CT-based COVID-19 Triage: Deep Multitask Learning Improves Joint Identification and Severity Quantification

arXiv: https://arxiv.org/abs/2006.01441 · HTML: https://mlsanity.com/paper/2006.01441/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2020.

## Built on by (most cited first, 10 of 10)

- [Detection and severity classification of COVID-19 in CT images using deep learning](https://mlsanity.com/paper/2102.07726/) (2021)
- [Multi-task learning with cross-task consistency for improved depth estimation in colonoscopy](https://mlsanity.com/paper/2311.18664/) (2023)
- [Probabilistically Robust Watermarking of Neural Networks](https://mlsanity.com/paper/2401.08261/) (2024)
- [DenResCov-19: A deep transfer learning network for robust automatic classification of COVID-19, pneumonia, and tuberculosis from X-rays](https://mlsanity.com/paper/2104.04006/) (2021)
- [U-GAT: Multimodal Graph Attention Network for COVID-19 Outcome Prediction](https://mlsanity.com/paper/2108.00860/) (2021)
- [Zero-Shot Domain Adaptation in CT Segmentation by Filtered Back Projection Augmentation](https://mlsanity.com/paper/2107.08543/) (2021)
- [Adaptation to CT Reconstruction Kernels by Enforcing Cross-domain Feature Maps Consistency](https://mlsanity.com/paper/2203.14616/) (2022)
- [Full-scale Deeply Supervised Attention Network for Segmenting COVID-19 Lesions](https://mlsanity.com/paper/2210.15571/) (2022)
- [Development of a Multi-Task Learning V-Net for Pulmonary Lobar Segmentation on Computed Tomography and Application to Diseased Lungs](https://mlsanity.com/paper/2105.05204/) (2021)
- [Composite Deep Network with Feature Weighting for Improved Delineation of COVID Infection in Lung CT](https://mlsanity.com/paper/2301.06961/) (2023)

## Abstract (arXiv)

The current COVID-19 pandemic overloads healthcare systems, including radiology departments. Though several deep learning approaches were developed to assist in CT analysis, nobody considered study triage directly as a computer science problem. We describe two basic setups: Identification of COVID-19 to prioritize studies of potentially infected patients to isolate them as early as possible; Severity quantification to highlight studies of severe patients and direct them to a hospital or provide emergency medical care. We formalize these tasks as binary classification and estimation of affected lung percentage. Though similar problems were well-studied separately, we show that existing methods provide reasonable quality only for one of these setups. We employ a multitask approach to consolidate both triage approaches and propose a convolutional neural network to combine all available labels within a single model. In contrast with the most popular multitask approaches, we add classification layers to the most spatially detailed upper part of U-Net instead of the bottom, less detailed latent representation. We train our model on approximately 2000 publicly available CT studies and test it with a carefully designed set consisting of 32 COVID-19 studies, 30 cases with bacterial pneumonia, 31 healthy patients, and 30 patients with other lung pathologies to emulate a typical patient flow in an out-patient hospital. The proposed multitask model outperforms the latent-based one and achieves ROC AUC scores ranging from 0.87+-01 (bacterial pneumonia) to 0.97+-01 (healthy controls) for Identification of COVID-19 and 0.97+-01 Spearman Correlation for Severity quantification. We release all the code and create a public leaderboard, where other community members can test their models on our test dataset.