Work

Enhanced Performance of Prostate Cancer Aggressiveness Prediction through Fusion of Multi-parametric MR Images and Additional Training of a Self-supervised Learning Model

Year: 2024

Type: article

Abstract: This study proposes a method to improve cancer aggressiveness prediction performance by fusing multi-parametric MR images in various ways and additionally training a self-supervised learning model with data used for prostate cancer aggressiveness prediction. Experimental results showed that the prediction performance of prostate cancer aggressiveness improved when using multi-parametric MR images compared to using single-parametric MR image. The prediction fusion method of majority voting method with multi-parametric MR images showed the highest performance. Furthermore, the model with additional self-supervised learning on the dataset used for prostate cancer aggressiveness prediction showed an average performance improvement of 0.8% in accuracy, 4.49% in sensitivity, 1.77% in negative predictive value, and 0.02 in AUROC score across all single and multi-parametric MR image experiments.

Source: Journal of the Korea Computer Graphics Society

DOI: 10.15701/kcgs.2024.30.5.1

Authors: Yejin Shin, Min Jin Lee, Sung Il Hwang, Helen Hong

Institutions: Seoul Women's University, Seoul National University Bundang Hospital

Language: English

Topic: Radiomics and Machine Learning in Medical Imaging

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: diamond

Funders: Ministry of Health and Welfare, National Research Foundation of Korea, Ministry of Science ICT and Future Planning