Chest X-rays have been widely used for COVID-19 screening; however, 3D computed tomography (CT) is a more effective modality. We present our findings on COVID-19 severity prediction from chest CT scans using the STOIC dataset. We developed an ensemble deep learning based model that incorporates multiple neural networks to improve predictions. To address data imbalance, we used slicing functions and data augmentation. We further improved performance using test time data augmentation. Our approach which employs a simple yet effective ensemble of deep learning-based models with strong test time augmentations, achieved results comparable to more complex methods and secured the fourth position in the STOIC2021 COVID-19 AI Challenge. Our code is available on online: at: https://github.com/aleemsidra/stoic2021- baseline-finalphase-main.

翻译：胸部X光已广泛用于COVID-19筛查，但三维计算机断层扫描（CT）是一种更有效的检查手段。我们展示了基于STOIC数据集从胸部CT扫描预测COVID-19严重程度的研究成果。我们开发了一种集成深度学习模型，该模型融合了多个神经网络以提高预测性能。为应对数据不平衡问题，我们采用了切片函数和数据增强技术，并通过测试时数据增强进一步提升了效果。我们的方法采用了简单而有效的深度学习模型集成策略，结合强大的测试时增强技术，取得了与复杂方法相当的结果，并在STOIC2021 COVID-19人工智能挑战赛中荣获第四名。相关代码已开源，地址为：https://github.com/aleemsidra/stoic2021- baseline-finalphase-main。