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Related Concept Videos

Imaging Studies II: Ultrasonography01:24

Imaging Studies II: Ultrasonography

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IntroductionUltrasonography, or renal ultrasound, is a noninvasive medical imaging technique that uses high-frequency sound waves to visualize the kidneys, ureters, bladder, and surrounding tissues.Indications for Urinary System UltrasonographyUrinary system ultrasonography is indicated in various clinical scenarios, such as:Kidney Stones (Urolithiasis): To detect and monitor the size and presence of kidney or urinary tract stones.Hydronephrosis: To assess the dilation of the renal pelvis and...
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Ultrasonography01:17

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Ultrasonography is an imaging technique that uses high-frequency sound waves to visualize the body's internal structures. It is a non-invasive and safe procedure that does not involve the use of ionizing radiation, making it widely used in various medical fields. Ultrasonography is used to study heart function, blood flow in the neck or extremities, certain conditions such as gallbladder disease, and fetal growth and development.
During an ultrasonography procedure, a handheld device called...
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Related Experiment Video

Updated: Jan 9, 2026

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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Multimodal Deep Learning-Based Classification of Breast Non-Mass Lesions Using Gray Scale and Color Doppler

Tianjiao Wang1, Qingli Zhu1, Tianxiang Yu2

  • 1Department of Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China.

Diagnostics (Basel, Switzerland)
|December 11, 2025
PubMed
Summary
This summary is machine-generated.

A new multimodal deep learning method using VGG16 effectively classifies breast non-mass lesions (NMLs) from ultrasound images. This approach significantly improves diagnostic accuracy compared to single-modality models, aiding radiologists in distinguishing benign from malignant NMLs.

Keywords:
artificial intelligencebreast diseasebreast non-mass lesioncomputer-aided diagnosisultrasound imaging

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Breast non-mass lesions (NMLs) present diagnostic challenges.
  • Accurate classification of NMLs is crucial for timely and appropriate patient management.
  • Current ultrasound (US) methods have limitations in differentiating benign from malignant NMLs.

Purpose of the Study:

  • To develop and evaluate a multimodal deep learning model for classifying benign and malignant breast NMLs.
  • To compare the diagnostic performance of multimodal US models against single-modality US models.
  • To leverage both grayscale and color Doppler US images for enhanced NML classification.

Main Methods:

  • A retrospective study involving 248 pathologically confirmed NMLs from 241 female patients.
  • Evaluation of three Convolutional Neural Network (CNN) architectures (ResNet50, ResNet18, VGG16) for single-modality classification (grayscale or color Doppler US).
  • Development of a multimodal deep learning model by concatenating features from optimal single-modality CNNs (VGG16) for combined grayscale and color Doppler US analysis.

Main Results:

  • Single-modality VGG16 models demonstrated superior performance over ResNet models for both grayscale and color Doppler US.
  • Grayscale US models outperformed color Doppler US models in single-modality classification.
  • The multimodal VGG16 model achieved high diagnostic performance: 91.54% accuracy, 94.15% sensitivity, 87.30% specificity, 0.93 F1 score, and 0.96 AUC, outperforming single-modality models.

Conclusions:

  • VGG16-based multimodal deep learning offers excellent diagnostic efficacy for distinguishing benign from malignant breast NMLs.
  • The proposed multimodal approach shows significant potential to assist radiologists in NML assessment.
  • This method enhances the diagnostic capabilities of breast ultrasound in NML classification.