Not All Areas Are Equal: Detecting Thoracic Disease with ChestWNet

Zhou Yang, Zhenhe Pan, Sisheng Liang, Fang Jin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Automating pneumonia diagnosis from X-ray images could significantly improve patient diagnosing outcomes. A major challenge is that disease information (features) must be extracted directly from the image backgrounds. Motivated by recent advances in Convolutional Neural Network (CNN), we propose a hierarchical weighting deep learning model, ChestWNet, that combines DenseNet and transfer learning to detect and localize thoracic diseases from chest x-rays. Hierarchical weighting networks are designed to assign scores reflecting the importance of specific pixels (regions), and learning weights at pixel-, region-, and image-levels, jointly learning these hierarchical weighting networks and the image classification network in an end-to-end manner. Chest X-ray datasets are customized to solve the unbalancing label problem in these datasets. Extensive experiments show that ChestWNet significantly outperforms other established prediction methods, and can also be applied to similar scenarios with fixed point-of-interest regions in images.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE International Conference on Big Data, Big Data 2020
EditorsXintao Wu, Chris Jermaine, Li Xiong, Xiaohua Tony Hu, Olivera Kotevska, Siyuan Lu, Weijia Xu, Srinivas Aluru, Chengxiang Zhai, Eyhab Al-Masri, Zhiyuan Chen, Jeff Saltz
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3447-3452
Number of pages6
ISBN (Electronic)9781728162515
DOIs
StatePublished - Dec 10 2020
Event8th IEEE International Conference on Big Data, Big Data 2020 - Virtual, Atlanta, United States
Duration: Dec 10 2020Dec 13 2020

Publication series

NameProceedings - 2020 IEEE International Conference on Big Data, Big Data 2020

Conference

Conference8th IEEE International Conference on Big Data, Big Data 2020
Country/TerritoryUnited States
CityVirtual, Atlanta
Period12/10/2012/13/20

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