ThoraX-PriorNet: A Novel Attention-Based Architecture Using Anatomical Prior Probability Maps for Thoracic Disease Classification
<p dir="ltr">Computer-aided disease diagnosis and prognosis based on medical images is a rapidly emerging field. Many Convolutional Neural Network (CNN) architectures have been developed by researchers for disease classification and localization from chest X-ray images. It is known t...
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2024
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| _version_ | 1864513544631877632 |
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| author | Md. Iqbal Hossain (723299) |
| author2 | Mohammad Zunaed (21734633) Md. Kawsar Ahmed (21734636) S. M. Jawwad Hossain (21734639) Anwarul Hasan (1332066) Taufiq Hasan (17949200) |
| author2_role | author author author author author |
| author_facet | Md. Iqbal Hossain (723299) Mohammad Zunaed (21734633) Md. Kawsar Ahmed (21734636) S. M. Jawwad Hossain (21734639) Anwarul Hasan (1332066) Taufiq Hasan (17949200) |
| author_role | author |
| dc.creator.none.fl_str_mv | Md. Iqbal Hossain (723299) Mohammad Zunaed (21734633) Md. Kawsar Ahmed (21734636) S. M. Jawwad Hossain (21734639) Anwarul Hasan (1332066) Taufiq Hasan (17949200) |
| dc.date.none.fl_str_mv | 2024-01-09T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2023.3346315 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/ThoraX-PriorNet_A_Novel_Attention-Based_Architecture_Using_Anatomical_Prior_Probability_Maps_for_Thoracic_Disease_Classification/29590427 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Biomedical engineering Information and computing sciences Machine learning Anatomical prior anatomy-aware attention chest X-ray thoracic disease classification Diseases Lung X-ray imaging Location awareness Feature extraction Probabilistic logic Anatomy Classification algorithms Lesions |
| dc.title.none.fl_str_mv | ThoraX-PriorNet: A Novel Attention-Based Architecture Using Anatomical Prior Probability Maps for Thoracic Disease Classification |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Computer-aided disease diagnosis and prognosis based on medical images is a rapidly emerging field. Many Convolutional Neural Network (CNN) architectures have been developed by researchers for disease classification and localization from chest X-ray images. It is known that different thoracic disease lesions are more likely to occur in specific anatomical regions compared to others. This article aims to incorporate this disease and region-dependent prior probability distribution within a deep learning framework. We present the ThoraX-PriorNet, a novel attention-based CNN model for thoracic disease classification. We first estimate a disease-dependent spatial probability, i.e., an anatomical prior, that indicates the probability of occurrence of a disease in a specific region in a chest X-ray image. Next, we develop a novel attention-based classification model that combines information from the estimated anatomical prior and automatically extracted chest region of interest (ROI) masks to provide attention to the feature maps generated from a deep convolution network. Unlike previous works that utilize various self-attention mechanisms, the proposed method leverages the extracted chest ROI masks along with the probabilistic anatomical prior information, which selects the region of interest for different diseases to provide attention. The proposed method shows superior performance in disease classification on the NIH ChestX-ray14 dataset compared to existing state-of-the-art methods while reaching an area under the ROC curve (%AUC) of 84.67. Regarding disease localization, the anatomy prior attention method shows competitive performance compared to state-of-the-art methods, achieving an accuracy of 0.80, 0.63, 0.49, 0.33, 0.28, 0.21, and 0.04 with an Intersection over Union (IoU) threshold of 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, and 0.7, respectively. The proposed ThoraX-PriorNet can be generalized to different medical image classification and localization tasks where the probability of occurrence of the lesion is dependent on specific anatomical sites.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2023.3346315" target="_blank">https://dx.doi.org/10.1109/access.2023.3346315</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_03fc83ffcdeebdf46baf807f44c92440 |
| identifier_str_mv | 10.1109/access.2023.3346315 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29590427 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | ThoraX-PriorNet: A Novel Attention-Based Architecture Using Anatomical Prior Probability Maps for Thoracic Disease ClassificationMd. Iqbal Hossain (723299)Mohammad Zunaed (21734633)Md. Kawsar Ahmed (21734636)S. M. Jawwad Hossain (21734639)Anwarul Hasan (1332066)Taufiq Hasan (17949200)EngineeringBiomedical engineeringInformation and computing sciencesMachine learningAnatomical prioranatomy-aware attentionchest X-raythoracic disease classificationDiseasesLungX-ray imagingLocation awarenessFeature extractionProbabilistic logicAnatomyClassification algorithmsLesions<p dir="ltr">Computer-aided disease diagnosis and prognosis based on medical images is a rapidly emerging field. Many Convolutional Neural Network (CNN) architectures have been developed by researchers for disease classification and localization from chest X-ray images. It is known that different thoracic disease lesions are more likely to occur in specific anatomical regions compared to others. This article aims to incorporate this disease and region-dependent prior probability distribution within a deep learning framework. We present the ThoraX-PriorNet, a novel attention-based CNN model for thoracic disease classification. We first estimate a disease-dependent spatial probability, i.e., an anatomical prior, that indicates the probability of occurrence of a disease in a specific region in a chest X-ray image. Next, we develop a novel attention-based classification model that combines information from the estimated anatomical prior and automatically extracted chest region of interest (ROI) masks to provide attention to the feature maps generated from a deep convolution network. Unlike previous works that utilize various self-attention mechanisms, the proposed method leverages the extracted chest ROI masks along with the probabilistic anatomical prior information, which selects the region of interest for different diseases to provide attention. The proposed method shows superior performance in disease classification on the NIH ChestX-ray14 dataset compared to existing state-of-the-art methods while reaching an area under the ROC curve (%AUC) of 84.67. Regarding disease localization, the anatomy prior attention method shows competitive performance compared to state-of-the-art methods, achieving an accuracy of 0.80, 0.63, 0.49, 0.33, 0.28, 0.21, and 0.04 with an Intersection over Union (IoU) threshold of 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, and 0.7, respectively. The proposed ThoraX-PriorNet can be generalized to different medical image classification and localization tasks where the probability of occurrence of the lesion is dependent on specific anatomical sites.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2023.3346315" target="_blank">https://dx.doi.org/10.1109/access.2023.3346315</a></p>2024-01-09T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2023.3346315https://figshare.com/articles/journal_contribution/ThoraX-PriorNet_A_Novel_Attention-Based_Architecture_Using_Anatomical_Prior_Probability_Maps_for_Thoracic_Disease_Classification/29590427CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/295904272024-01-09T03:00:00Z |
| spellingShingle | ThoraX-PriorNet: A Novel Attention-Based Architecture Using Anatomical Prior Probability Maps for Thoracic Disease Classification Md. Iqbal Hossain (723299) Engineering Biomedical engineering Information and computing sciences Machine learning Anatomical prior anatomy-aware attention chest X-ray thoracic disease classification Diseases Lung X-ray imaging Location awareness Feature extraction Probabilistic logic Anatomy Classification algorithms Lesions |
| status_str | publishedVersion |
| title | ThoraX-PriorNet: A Novel Attention-Based Architecture Using Anatomical Prior Probability Maps for Thoracic Disease Classification |
| title_full | ThoraX-PriorNet: A Novel Attention-Based Architecture Using Anatomical Prior Probability Maps for Thoracic Disease Classification |
| title_fullStr | ThoraX-PriorNet: A Novel Attention-Based Architecture Using Anatomical Prior Probability Maps for Thoracic Disease Classification |
| title_full_unstemmed | ThoraX-PriorNet: A Novel Attention-Based Architecture Using Anatomical Prior Probability Maps for Thoracic Disease Classification |
| title_short | ThoraX-PriorNet: A Novel Attention-Based Architecture Using Anatomical Prior Probability Maps for Thoracic Disease Classification |
| title_sort | ThoraX-PriorNet: A Novel Attention-Based Architecture Using Anatomical Prior Probability Maps for Thoracic Disease Classification |
| topic | Engineering Biomedical engineering Information and computing sciences Machine learning Anatomical prior anatomy-aware attention chest X-ray thoracic disease classification Diseases Lung X-ray imaging Location awareness Feature extraction Probabilistic logic Anatomy Classification algorithms Lesions |