IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Sep 1, 2025
Ultrasonic attenuation can be used to characterize tissue properties of the human breast. Both quantitative ultrasound (QUS) and ultrasound tomography (USCT) can provide attenuation estimation. However, limitations have been identified for both appro...
Purpose To improve the generalizability of pathologic complete response prediction following neoadjuvant chemotherapy using deep learning-based retrospective pharmacokinetic quantification of early treatment dynamic contrast-enhanced MRI. Materials a...
OBJECTIVE: To develop an advanced deep learning model for accurate differentiation between benign and malignant masses in ultrasound breast cancer screening, addressing the challenges of noise, blur, and complex tissue structures in ultrasound imagin...
Feature selection using evolutionary algorithms-a well-liked technique for choosing pertinent characteristics in huge datasets is explored. In machine learning, feature selection (FS) is a key phase that helps to boost model efficiency, decrease over...
OBJECTIVE: To develop a dynamic contrast-enhanced ultrasound (CEUS)-based method for segmenting tumor perfusion subregions, quantifying tumor heterogeneity, and constructing models for distinguishing benign from malignant breast tumors.
BACKGROUND: Recently, western countries have built evidence on mammographic artificial Intelligence-computer-aided diagnosis (AI-CADx) systems; however, their effectiveness has not yet been sufficiently validated in Japanese women. In this study, we ...
Breast cancer, the most commonly diagnosed disease worldwide, has been linked to the overexpression of the kinesin Eg5 protein, a spindle motor protein crucial for the assembly and maintenance of the bipolar spindle during mitosis. This makes Eg5 an ...
Purpose To develop and evaluate an open-source deep learning model for detection and localization of breast cancer on MRI scans. Materials and Methods In this retrospective study, a deep learning model for breast cancer detection and localization was...
Breast cancer is the leading cancer threatening women's health. In recent years, deep neural networks have outperformed traditional methods in terms of both accuracy and efficiency for breast cancer classification. However, most ultrasound-based brea...
RATIONALE AND OBJECTIVES: This study aimed to evaluate the interpretability and patient perception of AI-translated mammography and sonography reports, focusing on comprehensibility, follow-up recommendations, and conveyed empathy using a survey.
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