A deep-learning based diagnostic framework for Breast Cancer
Stavros Sykiotis, Ioannis N. Tzortzis, Aikaterini Angeli, Nikolaos D. Doulamis, Dimitris Kalogeras · 2022
In this paper, we present a deep-learning based diagnostic pipeline for breast cancer that has been designed in the H2020 INCISIVE project. The design of the pipeline has taken into consideration the needs of medical professionals and has been adapted to focus on early and accurate detection of malignant lesions to improve the patient’s survival rate. The main goal of our approach is to create a complete diagnostic service and bridge the gap towards real-world adoption of Artificial Intelligence on medical imaging. The pipeline will be offered as a service to medical professionals during the pilots of the project to evaluate its performance and assess the maturity of integrating such a service in a clinical workflow.