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Related Experiment Video

Updated: Sep 18, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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[Thyroid nodule segmentation method integrating receiving weighted key-value architecture and spherical geometric

Licheng Zhu1, Guohui Wei1

  • 1College of Medical Information Engineering, Shandong University of Traditional Chinese Medicine, Jinan 250355, P. R. China.

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi = Journal of Biomedical Engineering = Shengwu Yixue Gongchengxue Zazhi
|June 26, 2025
PubMed
Summary

This study introduces an efficient method for segmenting ultrasound thyroid nodules using RWKV architecture and SGF sampling. The approach enhances detail capture and reduces computational complexity for improved nodule segmentation accuracy.

Keywords:
Feature fusionImage segmentationReceiving weighted key-value architectureSpherical geometric featureThyroid nodules

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Artificial Intelligence

Context:

  • Transformer models face challenges in ultrasound thyroid nodule segmentation due to high computational complexity.
  • Traditional sampling methods can lose critical details in high-resolution ultrasound images, impacting nodule segmentation.
  • Accurate segmentation of thyroid nodules is crucial for diagnosis and treatment planning.

Purpose:

  • To develop a novel thyroid nodule segmentation method addressing computational complexity and detail loss in ultrasound images.
  • To integrate the receiving weighted key-value (RWKV) architecture with spherical geometry feature (SGF) sampling for enhanced segmentation.
  • To introduce a patch attention module (PAM) for optimizing feature map focus on high-resolution encoder features.

Summary:

  • The proposed method utilizes RWKV architecture and SGF sampling with 2D offset prediction and pixel-level adjustment for precise segmentation.
  • A patch attention module (PAM) refines decoder features via regional cross-attention, improving focus on encoder details.
  • Experiments on TN3K and DDTI datasets show superior Dice Similarity Coefficients (DSC) of 87.24% and 80.79% respectively, with lower computational cost.

Impact:

  • Achieves state-of-the-art performance in thyroid nodule segmentation with reduced computational complexity.
  • Offers a more efficient and accurate solution for segmenting complex ultrasound thyroid nodule images.
  • Potential to improve diagnostic accuracy and clinical workflow for thyroid nodule assessment.