mlsanityData to 13 Oct 2026

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

arXiv 2306.00034 ↗First posted 31 May 2023eess.IV cs.CVcited by 0

Not on the map. Why?

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.

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 31 of its 94 references that are in the map. First posted on arXiv 31 May 2023.

Builds on

31 of 94 references.

References · 31

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,950
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,043
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,656
  4. 2017Squeeze-and-Excitation Networkscited by 4,772
  5. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,287
  6. 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,554
  7. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,475
  8. 2016Instance Normalization: The Missing Ingredient for Fast Stylizationcited by 1,193
  9. 2021Transformers in Vision: A Surveycited by 1,010
  10. 2021UNETR: Transformers for 3D Medical Image Segmentationcited by 957
  11. 2016DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Networkcited by 356
  12. 2017Road Extraction by Deep Residual U-Netcited by 333
19 more
  1. 2021TransBTS: Multimodal Brain Tumor Segmentation Using Transformercited by 265
  2. 2021TransFuse: Fusing Transformers and CNNs for Medical Image Segmentationcited by 258
  3. 2021CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentationcited by 227
  4. 2015Brain Tumor Segmentation with Deep Neural Networkscited by 137
  5. 2020nnU-Net for Brain Tumor Segmentationcited by 103
  6. 2021LeViT-UNet: Make Faster Encoders with Transformer for Medical Image Segmentationcited by 85
  7. 2014A New 2.5D Representation for Lymph Node Detection using Random Sets of Deep Convolutional Neural Network Observationscited by 56
  8. 2018Deep Neural Networks for Survival Analysis Based on a Multi-Task Frameworkcited by 44
  9. 2017An Exploration of 2D and 3D Deep Learning Techniques for Cardiac MR Image Segmentationcited by 36
  10. 2018Brain Tumor Segmentation using an Ensemble of 3D U-Nets and Overall Survival Prediction using Radiomic Featurescited by 33
  11. 2022Overview of the HECKTOR Challenge at MICCAI 2021: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Imagescited by 29
  12. 2021Squeeze-and-Excitation Normalization for Automated Delineation of Head and Neck Primary Tumors in Combined PET and CT Imagescited by 20
  13. 2021Can An Image Classifier Suffice For Action Recognition?cited by 20
  14. 2022TMSS: An End-to-End Transformer-based Multimodal Network for Segmentation and Survival Predictioncited by 18
  15. 2022An Ensemble Approach for Patient Prognosis of Head and Neck Tumor Using Multimodal Datacited by 13
  16. 2022Automatic Segmentation of Head and Neck Tumor: How Powerful Transformers Are?cited by 6
  17. 2020Combining CNN and Hybrid Active Contours for Head and Neck Tumor Segmentation in CT and PET imagescited by 3
  18. 2021A Machine Learning Challenge for Prognostic Modelling in Head and Neck Cancer Using Multi-modal Datacited by 3
  19. 2021Exploring 2D Data Augmentation for 3D Monocular Object Detectioncited by 3

Built on by

Not cited yet.