# Two-Stream Deep Feature Modelling for Automated Video Endoscopy Data Analysis

arXiv: https://arxiv.org/abs/2007.05914 · HTML: https://mlsanity.com/paper/2007.05914/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jul 2020.

## Built on by (most cited first, 5 of 5)

- [Video Polyp Segmentation: A Deep Learning Perspective](https://mlsanity.com/paper/2203.14291/) (2022)
- [Frontiers in Intelligent Colonoscopy](https://mlsanity.com/paper/2410.17241/) (2024)
- [Continuous Human Action Recognition for Human-Machine Interaction: A Review](https://mlsanity.com/paper/2202.13096/) (2022)
- [Anomaly Detection in Retinal Images using Multi-Scale Deep Feature Sparse Coding](https://mlsanity.com/paper/2201.11506/) (2022)
- [Learning Through Guidance: Knowledge Distillation for Endoscopic Image Classification](https://mlsanity.com/paper/2308.08731/) (2023)

## Abstract (arXiv)

Automating the analysis of imagery of the Gastrointestinal (GI) tract captured during endoscopy procedures has substantial potential benefits for patients, as it can provide diagnostic support to medical practitioners and reduce mistakes via human error. To further the development of such methods, we propose a two-stream model for endoscopic image analysis. Our model fuses two streams of deep feature inputs by mapping their inherent relations through a novel relational network model, to better model symptoms and classify the image. In contrast to handcrafted feature-based models, our proposed network is able to learn features automatically and outperforms existing state-of-the-art methods on two public datasets: KVASIR and Nerthus. Our extensive evaluations illustrate the importance of having two streams of inputs instead of a single stream and also demonstrates the merits of the proposed relational network architecture to combine those streams.