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Force Classification01:22

Force Classification

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Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
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Related Experiment Video

Updated: Sep 16, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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Multimodal Deep Learning for Stage Classification of Head and Neck Cancer Using Masked Autoencoders and Vision

Anas Turki1, Ossama Alshabrawy1, Wai Lok Woo1

  • 1Department of Computer and Information Science, Faculty of Engineering and Environment, Northumbria University, Newcastle upon Tyne NE1 8ST, UK.

Cancers
|July 12, 2025
PubMed
Summary

This study improves head and neck cancer staging by combining imaging and clinical data with deep learning. The multimodal approach enhances accuracy for better treatment planning in head and neck squamous cell carcinoma (HNSCC).

Keywords:
AJCC staginghead and neck cancermasked autoencodermultimodal fusionradiomicsvision transformer

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Area of Science:

  • Oncology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Head and neck squamous cell carcinoma (HNSCC) is an aggressive malignancy requiring precise staging for effective treatment.
  • Current AJCC staging relies on clinical data, but integrating imaging features can improve accuracy.

Purpose of the Study:

  • To develop a multimodal deep learning framework for enhanced AJCC staging of HNSCC.
  • To integrate clinical and imaging data for more accurate cancer classification.

Main Methods:

  • Utilized a VGG16-based masked autoencoder (MAE) for self-supervised visual feature extraction.
  • Incorporated attention mechanisms (CBAM and BAM) to refine feature learning.
  • Employed an attention-weighted fusion network to combine image and clinical data.

Main Results:

  • Achieved approximately 80% accuracy for four-class classification and ~66% for five-class classification on HNSCC and HN1 datasets.
  • Demonstrated significant improvements in Area Under the Curve (AUC), particularly with BAM attention.
  • Confirmed that integrating clinical features substantially boosts stage-classification performance.

Conclusions:

  • The proposed multimodal deep learning pipeline effectively enhances HNSCC AJCC staging.
  • This approach sets a precedent for robust radiomics-based multimodal pipelines in cancer research.
  • Integration of diverse data types is crucial for advancing precision oncology.