Review Article Insight into Deep Learning for Glioma Medical Image Analysis
by Qingqing Lv1,2, Minghua Wu1,2*
1Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, China
2The Key Laboratory of Carcinogenesis of the Chinese Ministry of Health; The Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education; Cancer Research Institute, Central South University, Changsha, Hunan, China
*Corresponding author: Minghua Wu, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, China
Received Date: 06 October, 2023
Accepted Date: 13 October, 2023
Published Date: 17 October, 2023
Citation: Lv Q, Wu M (2023) Insight into Deep Learning for Glioma Medical Image Analysis. J Oncol Res Ther 8: 10188.DOI: https://doi.org/10.29011/2574-710X.10188
Abstract
Histopathological images contain rich phenotypic information that can be used to monitor the underlying mechanisms that lead to disease progression and patient survival outcomes. In recent years, deep learning has become the mainstream method of choice for analyzing and interpreting histological images. Histopathological diagnosis of gliomas is a labor-intensive and labor-intensive process. A common method is using deep learning to classify glioma patients or predict prognosis based on histopathological images. However, these technologies still face some key challenges as they move toward clinical application. This review starts with emerging deep learning frameworks and explores how deep learning models based on histopathological images can be applied to gliomas. We focus on multimodal deep learning applications, including genomic, transcriptomic, MRI, and clinical data. We discuss the challenges associated with the use of artificial intelligence and propose potential directions for deep learning based on histopathological images in gliomas.