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Ultrasonography

Ultrasonography

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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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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Predicting Breast Tumor Malignancy Using Deep ConvNeXt Radiomics and Quality-Based Score Pooling in Ultrasound

Mohamed A Hassanien¹, Vivek Kumar Singh², Domenec Puig¹

¹Department of Computer Engineering and Mathematics, Univerity Rovira i Virgili, 43007 Tarragona, Spain.

Diagnostics (Basel, Switzerland)

|May 28, 2022

Summary

This summary is machine-generated.

This study introduces a deep learning radiomics method using breast ultrasound sequences to improve breast cancer detection accuracy. The novel approach enhances diagnosis, particularly for dense breasts, by analyzing image sequences rather than single images.

Keywords:

CAD system breast cancer deep learning transformers ultrasound sequence

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Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Early breast cancer detection is crucial for reducing mortality rates.
Ultrasound imaging (US) aids diagnosis, especially in dense breasts.
Current computer-aided diagnosis (CAD) systems using single US images have limited accuracy due to image quality and tumor variability.

Purpose of the Study:

To propose a deep-learning-based radiomics method utilizing breast US sequences for enhanced breast cancer diagnosis.
To address limitations of single-image CAD systems, including tumor variability and image noise.

Main Methods:

Radiomic features extraction using a deep learning network (ConvNeXt), trained with a vision transformer approach.
An efficient pooling mechanism to integrate malignancy scores from US sequence frames, considering image quality statistics.
Visual interpretation of results.

Main Results:

The proposed method demonstrates competitive performance compared to existing CNN-based approaches.
Ablation studies and experimental results validate the effectiveness of the deep learning radiomics method.
The approach shows promise in overcoming challenges associated with US image analysis for breast cancer.

Conclusions:

The deep-learning-based radiomics method using breast US sequences offers an advanced approach for breast cancer diagnosis.
This method shows potential for improving diagnostic accuracy, especially in complex cases with dense breasts.
The integration of ConvNeXt and a novel pooling mechanism provides a robust framework for analyzing ultrasound sequences.