# Efficient, high-performance pancreatic segmentation using multi-scale feature extraction

arXiv: https://arxiv.org/abs/2009.00872 · HTML: https://mlsanity.com/paper/2009.00872/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Sep 2020.

## Built on by (most cited first, 1 of 1)

- [Deep Learning for Pancreas Segmentation: a Systematic Review](https://mlsanity.com/paper/2407.16313/) (2024)

## Abstract (arXiv)

For artificial intelligence-based image analysis methods to reach clinical applicability, the development of high-performance algorithms is crucial. For example, existent segmentation algorithms based on natural images are neither efficient in their parameter use nor optimized for medical imaging. Here we present MoNet, a highly optimized neural-network-based pancreatic segmentation algorithm focused on achieving high performance by efficient multi-scale image feature utilization.