BI-RADS-NET-V2: A Composite Multi-Task Neural Network for Computer-Aided Diagnosis of Breast Cancer in Ultrasound Images With Semantic and Quantitative Explanations
Computer-aided Diagnosis (CADx) based on explainable artificial intelligence (XAI) can gain the trust of radiologists and effectively improve diagnosis accuracy and consultation efficiency. This paper proposes BI-RADS-Net-V2, a novel machine learning approach for fully automatic breast cancer diagno...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10193749/ |
| _version_ | 1797746127597993984 |
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| author | Boyu Zhang Aleksandar Vakanski Min Xian |
| author_facet | Boyu Zhang Aleksandar Vakanski Min Xian |
| author_sort | Boyu Zhang |
| collection | DOAJ |
| description | Computer-aided Diagnosis (CADx) based on explainable artificial intelligence (XAI) can gain the trust of radiologists and effectively improve diagnosis accuracy and consultation efficiency. This paper proposes BI-RADS-Net-V2, a novel machine learning approach for fully automatic breast cancer diagnosis in ultrasound images. The BI-RADS-Net-V2 can accurately distinguish malignant tumors from benign ones and provides both semantic and quantitative explanations. The explanations are provided in terms of clinically proven morphological features used by clinicians for diagnosis and reporting mass findings, i.e., Breast Imaging Reporting and Data System (BI-RADS). The experiments on 1,192 Breast Ultrasound (BUS) images indicate that the proposed method improves the diagnosis accuracy by taking full advantage of the medical knowledge in BI-RADS while providing both semantic and quantitative explanations for the decision. |
| first_indexed | 2024-03-12T15:32:37Z |
| format | Article |
| id | doaj.art-94c28f7159ed40e5b20d3b37e271a7ae |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T15:32:37Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-94c28f7159ed40e5b20d3b37e271a7ae2023-08-09T23:01:39ZengIEEEIEEE Access2169-35362023-01-0111794807949410.1109/ACCESS.2023.329856910193749BI-RADS-NET-V2: A Composite Multi-Task Neural Network for Computer-Aided Diagnosis of Breast Cancer in Ultrasound Images With Semantic and Quantitative ExplanationsBoyu Zhang0https://orcid.org/0000-0002-9401-6163Aleksandar Vakanski1https://orcid.org/0000-0003-3365-1291Min Xian2https://orcid.org/0000-0001-6098-4441Institute for Interdisciplinary Data Sciences, University of Idaho, Moscow, ID, USADepartment of Nuclear Engineering and Industrial Management, University of Idaho, Idaho Falls, ID, USADepartment of Computer Science, University of Idaho, Idaho Falls, ID, USAComputer-aided Diagnosis (CADx) based on explainable artificial intelligence (XAI) can gain the trust of radiologists and effectively improve diagnosis accuracy and consultation efficiency. This paper proposes BI-RADS-Net-V2, a novel machine learning approach for fully automatic breast cancer diagnosis in ultrasound images. The BI-RADS-Net-V2 can accurately distinguish malignant tumors from benign ones and provides both semantic and quantitative explanations. The explanations are provided in terms of clinically proven morphological features used by clinicians for diagnosis and reporting mass findings, i.e., Breast Imaging Reporting and Data System (BI-RADS). The experiments on 1,192 Breast Ultrasound (BUS) images indicate that the proposed method improves the diagnosis accuracy by taking full advantage of the medical knowledge in BI-RADS while providing both semantic and quantitative explanations for the decision.https://ieeexplore.ieee.org/document/10193749/Breast cancercomputer-aided diagnosis (CADx)explainable artificial intelligencemulti-task learning |
| spellingShingle | Boyu Zhang Aleksandar Vakanski Min Xian BI-RADS-NET-V2: A Composite Multi-Task Neural Network for Computer-Aided Diagnosis of Breast Cancer in Ultrasound Images With Semantic and Quantitative Explanations IEEE Access Breast cancer computer-aided diagnosis (CADx) explainable artificial intelligence multi-task learning |
| title | BI-RADS-NET-V2: A Composite Multi-Task Neural Network for Computer-Aided Diagnosis of Breast Cancer in Ultrasound Images With Semantic and Quantitative Explanations |
| title_full | BI-RADS-NET-V2: A Composite Multi-Task Neural Network for Computer-Aided Diagnosis of Breast Cancer in Ultrasound Images With Semantic and Quantitative Explanations |
| title_fullStr | BI-RADS-NET-V2: A Composite Multi-Task Neural Network for Computer-Aided Diagnosis of Breast Cancer in Ultrasound Images With Semantic and Quantitative Explanations |
| title_full_unstemmed | BI-RADS-NET-V2: A Composite Multi-Task Neural Network for Computer-Aided Diagnosis of Breast Cancer in Ultrasound Images With Semantic and Quantitative Explanations |
| title_short | BI-RADS-NET-V2: A Composite Multi-Task Neural Network for Computer-Aided Diagnosis of Breast Cancer in Ultrasound Images With Semantic and Quantitative Explanations |
| title_sort | bi rads net v2 a composite multi task neural network for computer aided diagnosis of breast cancer in ultrasound images with semantic and quantitative explanations |
| topic | Breast cancer computer-aided diagnosis (CADx) explainable artificial intelligence multi-task learning |
| url | https://ieeexplore.ieee.org/document/10193749/ |
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