Yuan, Yuan, Ahn, Euijoon, Feng, Dagan, Khadra, Mohamad, and Kim, Jinman (2023) SSPT-bpMRI: A Self-supervised Pre-training Scheme for Improving Prostate Cancer Detection and Diagnosis in Bi-parametric MRI*. In: Proceedings of the 45th Auunual International Conference of the Engineering in Medicine & Biology Society. From: EMBC 2023: 45th Annual International Conference of the Engineering in Medicine & Biology Society, 24-27 July 2023, Sydney, NSW, Australia.

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Abstract

Prostate cancer (PCa) is one of the most prevalent cancers in men. Early diagnosis plays a pivotal role in reducing the mortality rate from clinically significant PCa (csPCa). In recent years, bi-parametric magnetic resonance imaging (bpMRI) has attracted great attention for the detection and diagnosis of csPCa. bpMRI is able to overcome some limitations of multi-parametric MRI (mpMRI) such as the use of contrast agents, the time-consuming for imaging and the costs, and achieve detection performance comparable to mpMRI. However, inter-reader agreements are currently low for prostate MRI. Advancements in artificial intelligence (AI) have propelled the development of deep learning (DL)-based computer-aided detection and diagnosis system (CAD). However, most of the existing DL models developed for csPCa identification are restricted by the scale of data and the scarcity in labels. In this paper, we propose a self-supervised pre-training scheme named SSPT-bpMRI with an image restoration pretext task integrating four different image transformations to improve the performance of DL algorithms. Specially, we explored the potential value of the self-supervised pre-training in fully supervised and weakly supervised situations. Experiments on the publicly available PI-CAI dataset demonstrate that our model outperforms the fully supervised or weakly supervised model alone.

Item ID:	81490
Item Type:	Conference Item (Research - E1)
ISBN:	979-8-3503-2447-1
Copyright Information:	This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/
Funders:	Australian Research Council (ARC)
Date Deposited:	02 Jan 2024 22:12
FoR Codes:	46 INFORMATION AND COMPUTING SCIENCES > 4603 Computer vision and multimedia computation > 460306 Image processing @ 70% 32 BIOMEDICAL AND CLINICAL SCIENCES > 3299 Other biomedical and clinical sciences > 329999 Other biomedical and clinical sciences not elsewhere classified @ 30%
SEO Codes:	28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280103 Expanding knowledge in the biomedical and clinical sciences @ 30% 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280115 Expanding knowledge in the information and computing sciences @ 70%
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