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, incorporating an
indicator function, to exploit multi-dataset integration. The effectiveness and
robustness of the proposed approach are demonstrated through significant
performance improvements in prognosis classification tasks across 18 different
convolutional neural network backbones in different evaluation strategies. This
improvement is evident over single-task baselines and standard transfer
learning strategies, supported by extensive statistical analysis, showing great
application potential.

利用人工智能从胸部放射影像中预测 COVID-19 病情结果是当前对抗 COVID-19 大流行的一个重要科学目标。本研究提出了一种新的多数据集多任务训练框架，通过整合来自不同来源、与常规多任务学习方法不同的相关数据集，预测 COVID-19 胸部 X 光片的预后结果，该框架假设评估严重程度有助于模型对预后严重程度组进行分类，从而提高其鲁棒性和预测能力。通过对 18 种不同的卷积神经网络骨干进行不同的评估策略，在预后分类任务中显著改进了性能，这是显而易见的，相比于单一任务基准和标准迁移学习策略，在广泛的统计分析支持下，显示出巨大的应用潜力。