Dual attention multiple instance learning with unsupervised complementary loss for COVID-19 screening.

Medical Image Analysis
Philip ChikontweSang Hyun Park

Abstract

Chest computed tomography (CT) based analysis and diagnosis of the Coronavirus Disease 2019 (COVID-19) plays a key role in combating the outbreak of the pandemic that has rapidly spread worldwide. To date, the disease has infected more than 18 million people with over 690k deaths reported. Reverse transcription polymerase chain reaction (RT-PCR) is the current gold standard for clinical diagnosis but may produce false positives; thus, chest CT based diagnosis is considered more viable. However, accurate screening is challenging due to the difficulty in annotation of infected areas, curation of large datasets, and the slight discrepancies between COVID-19 and other viral pneumonia. In this study, we propose an attention-based end-to-end weakly supervised framework for the rapid diagnosis of COVID-19 and bacterial pneumonia based on multiple instance learning (MIL). We further incorporate unsupervised contrastive learning for improved accuracy with attention applied both in spatial and latent contexts, herein we propose Dual Attention Contrastive based MIL (DA-CMIL). DA-CMIL takes as input several patient CT slices (considered as bag of instances) and outputs a single label. Attention based pooling is applied to implicitly select...Continue Reading

References

May 29, 2015·Nature·Yann LeCunGeoffrey Hinton
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Aug 5, 2017·Medical Image Analysis·Geert LitjensClara I Sánchez
Feb 8, 2020·JAMA : the Journal of the American Medical Association·Dawei WangZhiyong Peng
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May 20, 2020·Nature Medicine·Xueyan MeiYang Yang
Jul 31, 2020·IEEE Transactions on Medical Imaging·Zhongyi HanWei Zhang
Jul 31, 2020·IEEE Transactions on Medical Imaging·Xi OuyangDinggang Shen
Mar 12, 2021·IEEE/ACM Transactions on Computational Biology and Bioinformatics·Ying SongYuedong Yang
Mar 30, 2021·Radiology. Cardiothoracic Imaging·Ming-Yen NgMichael D Kuo
Mar 30, 2021·Radiology. Cardiothoracic Imaging·Lu HuangLiming Xia

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Software Mentioned

Trans
Zhang3DCNN
DeCovNet
Self
SimCLR
ResNeSt
DeepAttentionMIL
DeepPneumonia
CMIL
ZhangCNN

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