TY - JOUR AU - He, Xin AU - Wang, Shihao AU - Chu, Xiaowen AU - Shi, Shaohuai AU - Tang, Jiangping AU - Liu, Xin AU - Yan, Chenggang AU - Zhang, Jiyong AU - Ding, Guiguang PY - 2021/05/18 Y2 - 2024/03/28 TI - Automated Model Design and Benchmarking of Deep Learning Models for COVID-19 Detection with Chest CT Scans JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 35 IS - 6 SE - AAAI Technical Track Focus Area on AI Responses to the COVID-19 Pandemic DO - 10.1609/aaai.v35i6.16614 UR - https://ojs.aaai.org/index.php/AAAI/article/view/16614 SP - 4821-4829 AB - The COVID-19 pandemic has spread globally for several months. Because its transmissibility and high pathogenicity seriously threaten people's lives, it is crucial to accurately and quickly detect COVID-19 infection. Many recent studies have shown that deep learning (DL) based solutions can help detect COVID-19 based on chest CT scans. However, most existing work focuses on 2D datasets, which may result in low quality models as the real CT scans are 3D images. Besides, the reported results span a broad spectrum on different datasets with a relatively unfair comparison. In this paper, we first use three state-of-the-art 3D models (ResNet3D101, DenseNet3D121, and MC3\_18) to establish the baseline performance on three publicly available chest CT scan datasets. Then we propose a differentiable neural architecture search (DNAS) framework to automatically search the 3D DL models for 3D chest CT scans classification and use the Gumbel Softmax technique to improve the search efficiency. We further exploit the Class Activation Mapping (CAM) technique on our models to provide the interpretability of the results. The experimental results show that our searched models (CovidNet3D) outperform the baseline human-designed models on three datasets with tens of times smaller model size and higher accuracy. Furthermore, the results also verify that CAM can be well applied in CovidNet3D for COVID-19 datasets to provide interpretability for medical diagnosis. Code: https://github.com/HKBU-HPML/CovidNet3D. ER -