# Spectral-Spatial Recurrent-Convolutional Networks for In-Vivo Hyperspectral Tumor Type Classification

arXiv: https://arxiv.org/abs/2007.01042 · HTML: https://mlsanity.com/paper/2007.01042/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2020.

## Built on by (most cited first, 3 of 3)

- [Intra-operative Brain Tumor Detection with Deep Learning-Optimized Hyperspectral Imaging](https://mlsanity.com/paper/2302.02884/) (2023)
- [MDN: Mamba-Driven Dualstream Network For Medical Hyperspectral Image Segmentation](https://mlsanity.com/paper/2502.17255/) (2025)
- [Omni-Fusion of Spatial and Spectral for Hyperspectral Image Segmentation](https://mlsanity.com/paper/2507.06606/) (2025)

## Abstract (arXiv)

Early detection of cancerous tissue is crucial for long-term patient survival. In the head and neck region, a typical diagnostic procedure is an endoscopic intervention where a medical expert manually assesses tissue using RGB camera images. While healthy and tumor regions are generally easier to distinguish, differentiating benign and malignant tumors is very challenging. This requires an invasive biopsy, followed by histological evaluation for diagnosis. Also, during tumor resection, tumor margins need to be verified by histological analysis. To avoid unnecessary tissue resection, a non-invasive, image-based diagnostic tool would be very valuable. Recently, hyperspectral imaging paired with deep learning has been proposed for this task, demonstrating promising results on ex-vivo specimens. In this work, we demonstrate the feasibility of in-vivo tumor type classification using hyperspectral imaging and deep learning. We analyze the value of using multiple hyperspectral bands compared to conventional RGB images and we study several machine learning models' ability to make use of the additional spectral information. Based on our insights, we address spectral and spatial processing using recurrent-convolutional models for effective spectral aggregating and spatial feature learning. Our best model achieves an AUC of 76.3%, significantly outperforming previous conventional and deep learning methods.