Trends in deep learning for medical hyperspectral image analysis

Abstract

Deep learning algorithms have seen acute growth of interest in their applications throughout several fields of interest in the last decade, with medical hyperspectral imaging being a particularly promising domain. So far, to the best of our knowledge, there is no review paper that discusses the implementation of deep learning for medical hyperspectral imaging, which is what this review paper aims to accomplish by examining publications that currently utilize deep learning to perform effective analysis of medical hyperspectral imagery. This paper discusses deep learning concepts that are relevant and applicable to medical hyperspectral imaging analysis, several of which have been implemented since the boom in deep learning. This will comprise of reviewing the use of deep learning for classification, segmentation, and detection in order to investigate the analysis of medical hyperspectral imaging. Lastly, we discuss the current and future challenges pertaining to this discipline and the possible efforts to overcome such trials.

References in corpus (9)

• A Survey on Deep Learning in Medical Image Analysis
• U-Net and its variants for medical image segmentation: theory and applications
• CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images
• Comparative Study of CNN and RNN for Natural Language Processing
• The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches
• On the Origin of Deep Learning
• Speech-Driven Facial Reenactment Using Conditional Generative Adversarial Networks
• Estimation of Tissue Oxygen Saturation from RGB images and Sparse Hyperspectral Signals based on Conditional Generative Adversarial Network
• Probe-based Rapid Hybrid Hyperspectral and Tissue Surface Imaging Aided by Fully Convolutional Networks

Cited by in corpus (7)

• Robust deep learning-based semantic organ segmentation in hyperspectral images
• AI-powered skin spectral imaging enables instant sepsis diagnosis and outcome prediction in critically ill patients
• A transportable hyperspectral imaging setup based on fast, high-density spectral scanning for in situ quantitative biochemical mapping of fresh tissue biopsies
• Hyperspectral Image Segmentation: A Preliminary Study on the Oral and Dental Spectral Image Database (ODSI-DB)
• OOD-SEG: Exploiting out-of-distribution detection techniques for learning image segmentation from sparse multi-class positive-only annotations
• Tree-based Semantic Losses: Application to Sparsely-supervised Large Multi-class Hyperspectral Segmentation
• Label tree semantic losses for rich multi-class medical image segmentation