دورية أكاديمية

A Practical Roadmap to Implementing Deep Learning Segmentation in the Clinical Neuroimaging Research Workflow.

التفاصيل البيبلوغرافية
العنوان: A Practical Roadmap to Implementing Deep Learning Segmentation in the Clinical Neuroimaging Research Workflow.
المؤلفون: Pérez Cáceres, Marco, Gauvin, Alexandre, Dumais, Félix, Iorio-Morin, Christian
المصدر: World Neurosurg ; ISSN:1878-8769
بيانات النشر: Elsevier Science
سنة النشر: 2024
المجموعة: PubMed Central (PMC)
مصطلحات موضوعية: Deep Learning, Neuroimaging, Segmentation
الوصف: Thanks to the proliferation of open-source tools, we are seeing an exponential growth of machine learning applications, and its integration has become more accessible, particularly for segmentation tools in neuroimaging. This article explores a generalised methodology that harnesses these tools and aims/enables to expedite and enhance the reproducibility of clinical research. Herein, critical re considerations include hardware, software, neural network training strategies and data labelling guidelines. More specifically, we advocate an iterative approach to model training and transfer learning, focusing on internal validation and outlier handling early in the labelling process and fine-tuning later on. The iterative refinement process allows experts to intervene and improve model reliability whilst cutting down on their time spent in manual work. A seamless integration of the final model's predictions into clinical research is proposed to ensure standardized and reproducible results. In short, this article provides a comprehensive framework for accelerating research using machine learning techniques for image segmentation.
نوع الوثيقة: article in journal/newspaper
اللغة: English
العلاقة: https://doi.org/10.1016/j.wneu.2024.06.026Test; https://pubmed.ncbi.nlm.nih.gov/38866234Test
DOI: 10.1016/j.wneu.2024.06.026
الإتاحة: https://doi.org/10.1016/j.wneu.2024.06.026Test
https://pubmed.ncbi.nlm.nih.gov/38866234Test
حقوق: Copyright © 2024. Published by Elsevier Inc.
رقم الانضمام: edsbas.95CD4F25
قاعدة البيانات: BASE