A deep-learning based diagnostic framework for Breast Cancer
Authors: 
Stavros Sykiotis, Ioannis Tzortzis, Aikaterini Angeli, Nikolaos Doulamis, Dimitrios KalogerasAuthors Info & Claims
Stavros Sykiotis, Ioannis Tzortzis, Aikaterini Angeli, Nikolaos Doulamis, Dimitrios KalogerasAuthors Info & ClaimsPages 641 - 645
Abstract
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.
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Published: 11 July 2022
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PETRA '22
PETRA '22: The15th International Conference on PErvasive Technologies Related to Assistive Environments
June 29 - July 1, 2022
Corfu, Greece
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