{'fraction_non_alphanumeric': 0.024953789279112754, 'fraction_numerical': 0.0018484288354898336, 'mean_word_length': 5.603658536585366, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Breast cancer classification from digital breast tomosynthesis using 3D multi-subvolume approach

Digital mammography (DM) was the most common image guided diagnostic tool in breast cancer detection up till recent years. However, digital breast tomosynthesis (DBT) imaging, which presents more accurate results than DM, is going to replace DM in clinical practice. As in many medical image processing applications, Artificial Intelligence (AI) has been shown promising in reducing radiologists reading time with enhanced cancer diagnostic accuracy. In this paper, we implemented a 3D network using deep learning algorithms to detect breast cancer malignancy using DBT craniocaudal (CC) view images. We created a multi-sub-volume approach, in which the most representative slice (MRS) for malignancy scans is manually selected/defined by expert radiologists. We specifically compared the effects on different selections of the MRS by two radiologists and the resulting model performance variations. The results indicate that our scheme is relatively robust for all three experiments.