Automated Model Design and Benchmarking of Deep Learning Models for COVID-19 Detection with Chest CT Scans

Xin He, Shihao Wang, Xiaowen Chu*, Shaohuai Shi, Jiangping Tang, Xin Liu, Chenggang Yan, Jiyong Zhang*, Guiguang Ding

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

Abstract

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 the three publicly available chest CT scan datasets. Then we propose a differentiable neural architecture search (DNAS) framework to automatically search for the 3D DL models for 3D chest CT scans classification with the Gumbel Softmax technique to improve the searching efficiency. We further exploit the Class Acti vation Mapping (CAM) technique on our models to provide the interpretability of the results. The experimental results show that our automatically searched models (CovidNet3D) outperform the baseline human-designed models on the 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.
Original languageEnglish
Title of host publication35th AAAI Conference on Artificial Intelligence, AAAI 2021
PublisherAAAI press
Pages4821-4829
Number of pages9
ISBN (Print)9781577358664
DOIs
Publication statusPublished - 18 May 2021
Event35th AAAI Conference on Artificial Intelligence, AAAI 2021 - Virtual
Duration: 2 Feb 20219 Feb 2021
https://aaai.org/Conferences/AAAI-21/

Publication series

NameProceedings of the AAAI Conference on Artificial Intelligence
Number6
Volume35
ISSN (Print)2159-5399
ISSN (Electronic)2374-3468
NameAAAI-21/ IAAI-21/ EAAI-21 Proceedings

Conference

Conference35th AAAI Conference on Artificial Intelligence, AAAI 2021
Period2/02/219/02/21
Internet address

User-Defined Keywords

  • AI Responses to the COVID-19 Pandemic (Covid19)
  • Classification and Regression
  • Transfer/Adaptation/Multi-task/Meta/Automated Learning

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