Pang, Ting and Wong, Jeannie Hsiu Ding and Ng, Wei Lin and Chan, Chee Seng and Wang, Chang and Zhou, Xuezhi and Yu, Yi (2024) Radioport: a radiomics-reporting network for interpretable deep learning in BI-RADS classification of mammographic calcification. Physics in Medicine and Biology, 69 (6). 065006. ISSN 0031-9155, DOI https://doi.org/10.1088/1361-6560/ad2a95.
Full text not available from this repository.Abstract
Objective. Generally, due to a lack of explainability, radiomics based on deep learning has been perceived as a black-box solution for radiologists. Automatic generation of diagnostic reports is a semantic approach to enhance the explanation of deep learning radiomics (DLR). Approach. In this paper, we propose a novel model called radiomics-reporting network (Radioport), which incorporates text attention. This model aims to improve the interpretability of DLR in mammographic calcification diagnosis. Firstly, it employs convolutional neural networks to extract visual features as radiomics for multi-category classification based on breast imaging reporting and data system. Then, it builds a mapping between these visual features and textual features to generate diagnostic reports, incorporating an attention module for improved clarity. Main results. To demonstrate the effectiveness of our proposed model, we conducted experiments on a breast calcification dataset comprising mammograms and diagnostic reports. The results demonstrate that our model can: (i) semantically enhance the interpretability of DLR; and, (ii) improve the readability of generated medical reports. Significance. Our interpretable textual model can explicitly simulate the mammographic calcification diagnosis process.
| Item Type: | Article |
|---|---|
| Funders: | Ministry of Higher Education (Malaysia) Fundamental Research Grant Scheme, Major Science and Technology Project in Henan Province (China) (221100310500), Key Scientific Research Project of Universities in Henan Province (China) (23A413002); (FRGS/1/2019/SKK03/UM/01/1) |
| Uncontrolled Keywords: | interpretable deep learning; mammographic calcifications; explainable AI; automatic diagnostic report generation |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine |
| Divisions: | Faculty of Computer Science & Information Technology Faculty of Medicine |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 16 Oct 2024 08:06 |
| Last Modified: | 16 Oct 2024 08:06 |
| URI: | http://eprints.um.edu.my/id/eprint/45420 |
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