Detection and visualization of abnormality in chest radiographs using modality-specific convolutional neural network ensembles
Convolutional neural networks (CNNs) trained on natural images are extremely successful in image classification and localization due to superior automated feature extraction capability. In extending their use to biomedical recognition tasks, it is important to note that visual features of medical im...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2020-03-01
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| Series: | PeerJ |
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| Online Access: | https://peerj.com/articles/8693.pdf |
| _version_ | 1827610653506928640 |
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| author | Sivaramakrishnan Rajaraman Incheol Kim Sameer K. Antani |
| author_facet | Sivaramakrishnan Rajaraman Incheol Kim Sameer K. Antani |
| author_sort | Sivaramakrishnan Rajaraman |
| collection | DOAJ |
| description | Convolutional neural networks (CNNs) trained on natural images are extremely successful in image classification and localization due to superior automated feature extraction capability. In extending their use to biomedical recognition tasks, it is important to note that visual features of medical images tend to be uniquely different than natural images. There are advantages offered through training these networks on large scale medical common modality image collections pertaining to the recognition task. Further, improved generalization in transferring knowledge across similar tasks is possible when the models are trained to learn modality-specific features and then suitably repurposed for the target task. In this study, we propose modality-specific ensemble learning toward improving abnormality detection in chest X-rays (CXRs). CNN models are trained on a large-scale CXR collection to learn modality-specific features and then repurposed for detecting and localizing abnormalities. Model predictions are combined using different ensemble strategies toward reducing prediction variance and sensitivity to the training data while improving overall performance and generalization. Class-selective relevance mapping (CRM) is used to visualize the learned behavior of the individual models and their ensembles. It localizes discriminative regions of interest (ROIs) showing abnormal regions and offers an improved explanation of model predictions. It was observed that the model ensembles demonstrate superior localization performance in terms of Intersection of Union (IoU) and mean Average Precision (mAP) metrics than any individual constituent model. |
| first_indexed | 2024-03-09T07:53:12Z |
| format | Article |
| id | doaj.art-fe3b1e6eca7544c79c9e28229a19261d |
| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-03-09T07:53:12Z |
| publishDate | 2020-03-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ |
| spelling | doaj.art-fe3b1e6eca7544c79c9e28229a19261d2023-12-03T01:26:39ZengPeerJ Inc.PeerJ2167-83592020-03-018e869310.7717/peerj.8693Detection and visualization of abnormality in chest radiographs using modality-specific convolutional neural network ensemblesSivaramakrishnan Rajaraman0Incheol Kim1Sameer K. Antani2Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, Bethesda, MD, United States of AmericaLister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, Bethesda, MD, United States of AmericaLister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, Bethesda, MD, United States of AmericaConvolutional neural networks (CNNs) trained on natural images are extremely successful in image classification and localization due to superior automated feature extraction capability. In extending their use to biomedical recognition tasks, it is important to note that visual features of medical images tend to be uniquely different than natural images. There are advantages offered through training these networks on large scale medical common modality image collections pertaining to the recognition task. Further, improved generalization in transferring knowledge across similar tasks is possible when the models are trained to learn modality-specific features and then suitably repurposed for the target task. In this study, we propose modality-specific ensemble learning toward improving abnormality detection in chest X-rays (CXRs). CNN models are trained on a large-scale CXR collection to learn modality-specific features and then repurposed for detecting and localizing abnormalities. Model predictions are combined using different ensemble strategies toward reducing prediction variance and sensitivity to the training data while improving overall performance and generalization. Class-selective relevance mapping (CRM) is used to visualize the learned behavior of the individual models and their ensembles. It localizes discriminative regions of interest (ROIs) showing abnormal regions and offers an improved explanation of model predictions. It was observed that the model ensembles demonstrate superior localization performance in terms of Intersection of Union (IoU) and mean Average Precision (mAP) metrics than any individual constituent model.https://peerj.com/articles/8693.pdfDeep learningConvolutional neural networksModality-specific knowledgeChest X-raysEnsemble learningClass-selective relevance mapping |
| spellingShingle | Sivaramakrishnan Rajaraman Incheol Kim Sameer K. Antani Detection and visualization of abnormality in chest radiographs using modality-specific convolutional neural network ensembles PeerJ Deep learning Convolutional neural networks Modality-specific knowledge Chest X-rays Ensemble learning Class-selective relevance mapping |
| title | Detection and visualization of abnormality in chest radiographs using modality-specific convolutional neural network ensembles |
| title_full | Detection and visualization of abnormality in chest radiographs using modality-specific convolutional neural network ensembles |
| title_fullStr | Detection and visualization of abnormality in chest radiographs using modality-specific convolutional neural network ensembles |
| title_full_unstemmed | Detection and visualization of abnormality in chest radiographs using modality-specific convolutional neural network ensembles |
| title_short | Detection and visualization of abnormality in chest radiographs using modality-specific convolutional neural network ensembles |
| title_sort | detection and visualization of abnormality in chest radiographs using modality specific convolutional neural network ensembles |
| topic | Deep learning Convolutional neural networks Modality-specific knowledge Chest X-rays Ensemble learning Class-selective relevance mapping |
| url | https://peerj.com/articles/8693.pdf |
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