DeepMiCa: Automatic segmentation and classification of breast MIcroCAlcifications from mammograms

作者：Alessia Gerbasi, Greta Clementi, Fabio Corsi, S. Albasini, A. Malovini, S. Quaglini, R. Bellazzi · 发表于：Comput. Methods Programs Biomed. · 年份：2023 · DOI：10.1016/j.cmpb.2023.107483 · 被引用次数：36 · 研究领域：Computer Science、Medicine

BACKGROUND AND OBJECTIVE Breast cancer is the world's most prevalent form of cancer. The survival rates have increased in the last years mainly due to factors such as screening programs for early detection, new insights on the disease mechanisms as well as personalised treatments. Microcalcifications are the only first detectable sign of breast cancer and diagnosis timing is strongly related to the chances of survival. Nevertheless microcalcifications detection and classification as benign or malignant lesions is still a challenging clinical task and their malignancy can only be proven after a biopsy procedure. We propose DeepMiCa, a fully automated and visually explainable deep-learning based pipeline for the analysis of raw mammograms with microcalcifications. Our aim is to propose a reliable decision support system able to guide the diagnosis and help the clinicians to better inspect borderline difficult cases. METHODS DeepMiCa is composed by three main steps: (1) Preprocessing of the raw scans (2) Automatic patch-based Semantic Segmentation using a UNet based network with a custom loss function appositely designed to deal with extremely small lesions (3) Classification of the detected lesions with a deep transfer-learning approach. Finally, state-of-the-art explainable AI methods are used to produce maps for a visual interpretation of the classification results. Each step of DeepMiCa is designed to address the main limitations of the previous proposed works resulting in...