# Trends in deep learning for medical hyperspectral image analysis

arXiv: https://arxiv.org/abs/2011.13974 · HTML: https://mlsanity.com/paper/2011.13974/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Nov 2020.

## Built on by (most cited first, 13 of 13)

- [Hyperspectral Image Segmentation: A Preliminary Study on the Oral and Dental Spectral Image Database (ODSI-DB)](https://mlsanity.com/paper/2303.08252/) (2023)
- [FactoFormer: Factorized Hyperspectral Transformers with Self-Supervised Pretraining](https://mlsanity.com/paper/2309.09431/) (2023)
- [Forward-Forward Algorithm for Hyperspectral Image Classification: A Preliminary Study](https://mlsanity.com/paper/2307.00231/) (2023)
- [OOD-SEG: Exploiting out-of-distribution detection techniques for learning image segmentation from sparse multi-class positive-only annotations](https://mlsanity.com/paper/2411.09553/) (2024)
- [Robust Tumor Segmentation with Hyperspectral Imaging and Graph Neural Networks](https://mlsanity.com/paper/2311.11782/) (2023)
- [AI-powered skin spectral imaging enables instant sepsis diagnosis and outcome prediction in critically ill patients](https://mlsanity.com/paper/2408.09873/) (2024)
- [Tree-based Semantic Losses: Application to Sparsely-supervised Large Multi-class Hyperspectral Segmentation](https://mlsanity.com/paper/2506.21150/) (2025)
- [Deep Learning-Based Correction and Unmixing of Hyperspectral Images for Brain Tumor Surgery](https://mlsanity.com/paper/2402.03761/) (2024)
- [An Elliptic Kernel Unsupervised Autoencoder-Graph Convolutional Network Ensemble Model for Hyperspectral Unmixing](https://mlsanity.com/paper/2406.06742/) (2024)
- [Xeno-learning: knowledge transfer across species in deep learning-based spectral image analysis](https://mlsanity.com/paper/2410.19789/) (2024)
- [Dual-Domain Masked Image Modeling: A Self-Supervised Pretraining Strategy Using Spatial and Frequency Domain Masking for Hyperspectral Data](https://mlsanity.com/paper/2505.03220/) (2025)
- [Label tree semantic losses for rich multi-class medical image segmentation](https://mlsanity.com/paper/2507.15777/) (2025)
- [Adversarial Attacks on Medical Hyperspectral Imaging Exploiting Spectral-Spatial Dependencies and Multiscale Features](https://mlsanity.com/paper/2601.07056/) (2026)

## Abstract (arXiv)

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.