# Diagnosis and Prognosis of Head and Neck Cancer Patients using Artificial Intelligence

arXiv: https://arxiv.org/abs/2306.00034 · HTML: https://mlsanity.com/paper/2306.00034/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 31 of its 94 references that are in the map.
- First posted on arXiv 31 May 2023.

## Builds on (most cited first, 30 of 31)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation](https://mlsanity.com/paper/2102.04306/) (2021)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Instance Normalization: The Missing Ingredient for Fast Stylization](https://mlsanity.com/paper/1607.08022/) (2016)
- [Transformers in Vision: A Survey](https://mlsanity.com/paper/2101.01169/) (2021)
- [UNETR: Transformers for 3D Medical Image Segmentation](https://mlsanity.com/paper/2103.10504/) (2021)
- [DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Network](https://mlsanity.com/paper/1606.00931/) (2016)
- [Road Extraction by Deep Residual U-Net](https://mlsanity.com/paper/1711.10684/) (2017)
- [TransBTS: Multimodal Brain Tumor Segmentation Using Transformer](https://mlsanity.com/paper/2103.04430/) (2021)
- [TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation](https://mlsanity.com/paper/2102.08005/) (2021)
- [CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentation](https://mlsanity.com/paper/2103.03024/) (2021)
- [Brain Tumor Segmentation with Deep Neural Networks](https://mlsanity.com/paper/1505.03540/) (2015)
- [nnU-Net for Brain Tumor Segmentation](https://mlsanity.com/paper/2011.00848/) (2020)
- [LeViT-UNet: Make Faster Encoders with Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2107.08623/) (2021)
- [A New 2.5D Representation for Lymph Node Detection using Random Sets of Deep Convolutional Neural Network Observations](https://mlsanity.com/paper/1406.2639/) (2014)
- [Deep Neural Networks for Survival Analysis Based on a Multi-Task Framework](https://mlsanity.com/paper/1801.05512/) (2018)
- [An Exploration of 2D and 3D Deep Learning Techniques for Cardiac MR Image Segmentation](https://mlsanity.com/paper/1709.04496/) (2017)
- [Brain Tumor Segmentation using an Ensemble of 3D U-Nets and Overall Survival Prediction using Radiomic Features](https://mlsanity.com/paper/1812.01049/) (2018)
- [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)
- [Squeeze-and-Excitation Normalization for Automated Delineation of Head and Neck Primary Tumors in Combined PET and CT Images](https://mlsanity.com/paper/2102.10446/) (2021)
- [Can An Image Classifier Suffice For Action Recognition?](https://mlsanity.com/paper/2106.14104/) (2021)
- [TMSS: An End-to-End Transformer-based Multimodal Network for Segmentation and Survival Prediction](https://mlsanity.com/paper/2209.05036/) (2022)
- [An Ensemble Approach for Patient Prognosis of Head and Neck Tumor Using Multimodal Data](https://mlsanity.com/paper/2202.12537/) (2022)
- [Automatic Segmentation of Head and Neck Tumor: How Powerful Transformers Are?](https://mlsanity.com/paper/2201.06251/) (2022)
- [Combining CNN and Hybrid Active Contours for Head and Neck Tumor Segmentation in CT and PET images](https://mlsanity.com/paper/2012.14207/) (2020)
- [A Machine Learning Challenge for Prognostic Modelling in Head and Neck Cancer Using Multi-modal Data](https://mlsanity.com/paper/2101.11935/) (2021)

## Abstract (arXiv)

Cancer is one of the most life-threatening diseases worldwide, and head and neck (H&N) cancer is a prevalent type with hundreds of thousands of new cases recorded each year. Clinicians use medical imaging modalities such as computed tomography and positron emission tomography to detect the presence of a tumor, and they combine that information with clinical data for patient prognosis. The process is mostly challenging and time-consuming. Machine learning and deep learning can automate these tasks to help clinicians with highly promising results. This work studies two approaches for H&N tumor segmentation: (i) exploration and comparison of vision transformer (ViT)-based and convolutional neural network-based models; and (ii) proposal of a novel 2D perspective to working with 3D data. Furthermore, this work proposes two new architectures for the prognosis task. An ensemble of several models predicts patient outcomes (which won the HECKTOR 2021 challenge prognosis task), and a ViT-based framework concurrently performs patient outcome prediction and tumor segmentation, which outperforms the ensemble model.