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Weakly Supervised Deep Learning for Thoracic Disease Classification and Localization on Chest X-rays

Authors:

Junzhou HuangAuthors Info & Claims

BCB '18: Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics

Pages 103 - 110

https://doi.org/10.1145/3233547.3233573

Published: 15 August 2018 Publication History

Abstract

Chest X-rays is one of the most commonly available and affordable radiological examinations in clinical practice. While detecting thoracic diseases on chest X-rays is still a challenging task for machine intelligence, due to 1) the highly varied appearance of lesion areas on X-rays from patients of different thoracic disease and 2) the shortage of accurate pixel-level annotations by radiologists for model training. Existing machine learning methods are unable to deal with the challenge that thoracic diseases usually happen in localized disease-specific areas. In this article, we propose a weakly supervised deep learning framework equipped with squeeze-and-excitation blocks, multi-map transfer and max-min pooling for classifying common thoracic diseases as well as localizing suspicious lesion regions on chest X-rays. The comprehensive experiments and discussions are performed on the ChestX-ray14 dataset. Both numerical and visual results have demonstrated the effectiveness of proposed model and its better performance against the state-of-the-art pipelines.

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Cited By

Müller PMeissen FKaissis GRueckert D(2025)Weakly Supervised Object Detection in Chest X-Rays With Differentiable ROI Proposal Networks and Soft ROI PoolingIEEE Transactions on Medical Imaging10.1109/TMI.2024.343501544:1(221-231)Online publication date: Jan-2025
https://doi.org/10.1109/TMI.2024.3435015
Öztürk ŞTuralı MÇukur T(2025)HydraViT: Adaptive multi-branch transformer for multi-label disease classification from Chest X-ray imagesBiomedical Signal Processing and Control10.1016/j.bspc.2024.106959100(106959)Online publication date: Feb-2025
https://doi.org/10.1016/j.bspc.2024.106959
Pham TBrecheisen JWu CNguyen HDeng ZAdjeroh DDoretto GChoudhary ALe N(2025)ItpCtrl-AI: End-to-end interpretable and controllable artificial intelligence by modeling radiologists’ intentionsArtificial Intelligence in Medicine10.1016/j.artmed.2024.103054160(103054)Online publication date: Feb-2025
https://doi.org/10.1016/j.artmed.2024.103054
Show More Cited By

Index Terms

Weakly Supervised Deep Learning for Thoracic Disease Classification and Localization on Chest X-rays
1. Applied computing
  1. Life and medical sciences
    1. Computational biology
      1. Imaging
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Machine learning theory

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Published In

cover image ACM Conferences

BCB '18: Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics

August 2018

727 pages

ISBN:9781450357944

DOI:10.1145/3233547

General Chairs:
Amarda Shehu
George Mason University, USA
,
Cathy Wu
University of Delaware, USA
,
Program Chairs:
Christina Boucher
University of Florida, USA
,
Jing Li
Case Western Reserve University, USA
,
Hongfang Liu
Mayo Clinic, USA
,
Mihai Pop
University of Maryland, USA

Copyright © 2018 ACM.

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Published: 15 August 2018

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BCB '18: 9th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

August 29 - September 1, 2018

DC, Washington, USA

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BCB '18 Paper Acceptance Rate 46 of 148 submissions, 31%;

Overall Acceptance Rate 254 of 885 submissions, 29%

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Cited By

Müller PMeissen FKaissis GRueckert D(2025)Weakly Supervised Object Detection in Chest X-Rays With Differentiable ROI Proposal Networks and Soft ROI PoolingIEEE Transactions on Medical Imaging10.1109/TMI.2024.343501544:1(221-231)Online publication date: Jan-2025
https://doi.org/10.1109/TMI.2024.3435015
Öztürk ŞTuralı MÇukur T(2025)HydraViT: Adaptive multi-branch transformer for multi-label disease classification from Chest X-ray imagesBiomedical Signal Processing and Control10.1016/j.bspc.2024.106959100(106959)Online publication date: Feb-2025
https://doi.org/10.1016/j.bspc.2024.106959
Pham TBrecheisen JWu CNguyen HDeng ZAdjeroh DDoretto GChoudhary ALe N(2025)ItpCtrl-AI: End-to-end interpretable and controllable artificial intelligence by modeling radiologists’ intentionsArtificial Intelligence in Medicine10.1016/j.artmed.2024.103054160(103054)Online publication date: Feb-2025
https://doi.org/10.1016/j.artmed.2024.103054
Arora RSaini ISood N(2024)MULTI-LABEL CLASSIFICATION OF THORACIC DISEASES USING STRUCTURED DEEP LEARNING FRAMEWORKBiomedical Engineering: Applications, Basis and Communications10.4015/S101623722450006636:02Online publication date: 16-Feb-2024
https://doi.org/10.4015/S1016237224500066
Katona TTóth GPetró MHarangi B(2024)Developing New Fully Connected Layers for Convolutional Neural Networks with Hyperparameter Optimization for Improved Multi-Label Image ClassificationMathematics10.3390/math1206080612:6(806)Online publication date: 8-Mar-2024
https://doi.org/10.3390/math12060806
Katona TTóth GPetró MHarangi B(2024)Advanced Multi-Label Image Classification Techniques Using Ensemble MethodsMachine Learning and Knowledge Extraction10.3390/make60200606:2(1281-1297)Online publication date: 7-Jun-2024
https://doi.org/10.3390/make6020060
Shakya KAlavi APorteous JK PLaddi AJaiswal M(2024)A Critical Analysis of Deep Semi-Supervised Learning Approaches for Enhanced Medical Image ClassificationInformation10.3390/info1505024615:5(246)Online publication date: 24-Apr-2024
https://doi.org/10.3390/info15050246
Aasem MJaved Iqbal M(2024)Toward explainable AI in radiology: Ensemble-CAM for effective thoracic disease localization in chest X-ray images using weak supervised learningFrontiers in Big Data10.3389/fdata.2024.13664157Online publication date: 2-May-2024
https://doi.org/10.3389/fdata.2024.1366415
Pham TBrecheisen JNguyen ANguyen HLe N(2024)I-AI: A Controllable & Interpretable AI System for Decoding Radiologists’ Intense Focus for Accurate CXR Diagnoses2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)10.1109/WACV57701.2024.00767(7835-7844)Online publication date: 3-Jan-2024
https://doi.org/10.1109/WACV57701.2024.00767
Verma AKar AGhosh KDhara SSen DKumar Biswas P(2024)Artificially Generated Visual Scanpath Improves Multilabel Thoracic Disease Classification in Chest X-Ray ImagesIEEE Transactions on Instrumentation and Measurement10.1109/TIM.2024.342859173(1-11)Online publication date: 2024
https://doi.org/10.1109/TIM.2024.3428591
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