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Artificial intelligence-based systems to improve prostate cancer diagnosis using multiparametric magnetic resonance imaging

Rebecca Segre

Artificial intelligence-based systems to improve prostate cancer diagnosis using multiparametric magnetic resonance imaging.

Rel. Gabriella Balestra. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Biomedica, 2022

Abstract:

Prostate cancer is the second most common cancer among men. Multiparametric magnetic resonance imaging is a well-established tool for prostate cancer detection and diagnosis. However, image interpretation is complex, time-consuming and subject to high inter-reader variability. In this work, artificial intelligence methods are used to build a computer-aided detection system able to automatically discriminate between healthy and cancer-positive patients. In particular, a deep learning approach based on convolutional neural networks has been developed. The final result shows that the designed model presents good classification performances and is therefore well suited as a decision support tool. This thesis is the result of a seven-month stay at the Norwegian University of Science and Technology (NTNU), Trondheim, under the CIMORe group led by Professor Tone Frost Bathen.

Relators: Gabriella Balestra
Academic year: 2022/23
Publication type: Electronic
Number of Pages: 72
Additional Information: Tesi secretata. Fulltext non presente
Subjects:
Corso di laurea: Corso di laurea magistrale in Ingegneria Biomedica
Classe di laurea: New organization > Master science > LM-21 - BIOMEDICAL ENGINEERING
Aziende collaboratrici: NTNU
URI: http://webthesis.biblio.polito.it/id/eprint/24726
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