Classification of Prostate Cancer in 3D Magnetic Resonance Imaging Data based on Convolutional Neural Networks

被引：0

作者：

Rippa, Malte ^{[1
]}

Schulze, Ruben ^{[1
]}

Himstedt, Marian ^{[2
]}

Burn, Felice ^{[3
]}

机构：

[1] FUSE-AI GmbH, Grosser Burstah 44-46, Hamburg

[2] University of Lubeck, Ratzeburger Allee 160, Lubeck

[3] Cantonal Hospital Aarau, Tellstrasse 25, Aarau

来源：

Current Directions in Biomedical Engineering | 2024年 / 10卷 / 01期

关键词：

Computer Vision; Deep Learning; MRI; Prostate Cancer;

D O I：

10.1515/cdbme-2024-0116

中图分类号：

学科分类号：

摘要：

Prostate cancer is a commonly diagnosed cancerous disease among men world-wide. Even with modern technology such as multi-parametric magnetic resonance tomography and guided biopsies, the process for diagnosing prostate cancer remains time consuming and requires highly trained professionals. In this paper, different convolutional neural networks (CNN) are evaluated on their abilities to reliably classify whether an MRI sequence contains malignant lesions. Implementations of a ResNet, a ConvNet and a ConvNeXt for 3D image data are trained and evaluated. The models are trained using different data augmentation techniques, learning rates, and optimizers. The data is taken from a private dataset, provided by Cantonal Hospital Aarau. The best result was achieved with a ResNet3D, yielding an average precision score of 0.4583 and AUC ROC score of 0.6214. © 2024 by Walter de Gruyter Berlin/Boston.

引用

页码：61 / 64

页数：3

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