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Multi-Dataset Multi-Task Learning for COVID-19 Prognosis by Ruffini, Filippo; Tronchin, Lorenzo; Wu, Zhuoru; Chen, Wenting; Soda, Paolo; Shen, Linlin; Guarrasi, Valerio is a scholarly article available to read on EtoBox.

What is Multi-Dataset Multi-Task Learning for COVID-19 Prognosis about?

In the fight against the COVID-19 pandemic, leveraging artificial intelligence to predict disease outcomes from chest radiographic images represents a significant scientific aim. The challenge, however, lies in the scarcity of large, labeled datasets with compatible tasks for training deep learning models without leading to overfitting. Addressing this issue, we introduce a novel multi-dataset multi-task training framework that predicts COVID-19 prognostic outcomes from chest X-rays (CXR) by integrating correlated datasets from disparate sources, distant from conventional multi-task learning approaches, which rely on datasets with multiple and correlated labeling schemes. Our framework hypothesizes that assessing severity scores enhances the model's ability to classify prognostic severity groups, thereby improving its robustness and predictive power. The proposed architecture comprises a deep convolutional network that receives inputs from two publicly available CXR datasets, AIforCOVID for severity prognostic prediction and BRIXIA for severity score assessment, and branches into task-specific fully connected output networks. Moreover, we propose a multi-task loss function, incorpo

Author

Ruffini, Filippo; Tronchin, Lorenzo; Wu, Zhuoru; Chen, Wenting; Soda, Paolo; Shen, Linlin; Guarrasi, Valerio

Published

2024

Language

EN