# Multi-Scale Fusion Methodologies for Head and Neck Tumor Segmentation

arXiv: https://arxiv.org/abs/2210.16704 · HTML: https://mlsanity.com/paper/2210.16704/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 13 references that are in the map.
- First posted on arXiv 29 Oct 2022.

## Builds on (most cited first, 9 of 9)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [Transformers in Vision: A Survey](https://mlsanity.com/paper/2101.01169/) (2021)
- [Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images](https://mlsanity.com/paper/2201.01266/) (2022)
- [MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image Segmentation](https://mlsanity.com/paper/2105.07451/) (2021)
- [Overview of the HECKTOR Challenge at MICCAI 2021: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Images](https://mlsanity.com/paper/2201.04138/) (2022)
- [GMSRF-Net: An improved generalizability with global multi-scale residual fusion network for polyp segmentation](https://mlsanity.com/paper/2111.10614/) (2021)
- [An Efficient Multi-Scale Fusion Network for 3D Organ at Risk (OAR) Segmentation](https://mlsanity.com/paper/2208.07417/) (2022)

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

- [Selecting the Best Optimizers for Deep Learning based Medical Image Segmentation](https://mlsanity.com/paper/2302.02289/) (2023)

## Abstract (arXiv)

Head and Neck (H&N) organ-at-risk (OAR) and tumor segmentations are essential components of radiation therapy planning. The varying anatomic locations and dimensions of H&N nodal Gross Tumor Volumes (GTVn) and H&N primary gross tumor volume (GTVp) are difficult to obtain due to lack of accurate and reliable delineation methods. The downstream effect of incorrect segmentation can result in unnecessary irradiation of normal organs. Towards a fully automated radiation therapy planning algorithm, we explore the efficacy of multi-scale fusion based deep learning architectures for accurately segmenting H&N tumors from medical scans.