HIGHLIGHTS: • Intrabeam x-ray source and spherical applicators were simulated and skin dose was calculated. • Skin dose for constant skin to applicator distance strongly depends on applicator size. • Use of larger applicators generally results in higher skin dose. • The recommended 0.5-1 cm skin to applicator distance does not guarantee skin safety.
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 deep learning radiomics in mammographic calcification diagnosis. Firstly, it employs convolutional neural networks (CNN) to extract visual features as radiomics for multi-category classification based on Breast Imaging Reporting and Data System (BI-RADS). 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 deep learning radiomics; and, (ii) improve the readability of generated medical reports.
SIGNIFICANCE: Our interpretable textual model can explicitly simulate the mammographic calcification diagnosis process.