Semantic segmentation and object detection towards instance segmentation: Breast tumor identification

作     者：Mejri, Mohamed Mejri, Aymen Mejri, Oumayma Fekih, Chiraz 

作者机构：School of Electrical and Computer Engineering Georgia Institute of Technology United States Department of Digital Signal Processing Telecom Paris Paris France Department of Gynecology and Obstetrics Medicine School of Tunis Tunis Tunisia 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2021年

核心收录：

主　　题：Semantic Segmentation 

摘      要：Breast cancer is one of the factors that cause the increase of mortality of women. The most widely used method for diagnosing this geological disease i.e. breast cancer is the ultrasound scan. Several key features such as the smoothness and the texture of the tumor captured through ultrasound scans encode the abnormality of the breast tumors (malignant from benign). However, ultrasound scans are often noisy and include irrelevant parts of the breast that may bias the segmentation of eventual tumors. In this paper, we are going to extract the region of interest (i.e, bounding boxes of the tumors) and feed-forward them to one semantic segmentation encoder-decoder structure based on its classification (i.e, malignant or benign). the whole process aims to build an instance-based segmenter from a semantic segmenter and an object detector. © 2021, CC BY.