Publications by authors named "Yunfei Tong"
Background: Breast cancer is a major threat to women's health globally. Early detection of breast cancer is crucial for saving lives. One important early sign is the appearance of breast calcification in mammograms.
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- The study investigates the correlation between mammographic density in breast masses and whether they are benign or malignant, proposing a deep learning model (C2FTrans) to aid diagnosis based on this density.
- Researchers conducted a retrospective analysis involving 401 breast lesions (158 benign and 243 malignant), employing manual and computer-aided methods to assess mammographic density and various regions around the lesions.
- The results showed significant correlations between breast cancer likelihood, age, and mass density, with the developed model demonstrating high specificity and sensitivity for differentiating between benign and malignant lesions, indicating its potential as a diagnostic tool for radiologists.
Background: Architectural distortion (AD) is a common imaging manifestation of breast cancer, but is also seen in benign lesions. This study aimed to construct deep learning models using mask regional convolutional neural network (Mask-RCNN) for AD identification in full-field digital mammography (FFDM) and evaluate the performance of models for malignant AD diagnosis.
Methods: This retrospective diagnostic study was conducted at the Second Affiliated Hospital of Guangzhou University of Chinese Medicine between January 2011 and December 2020.
Purpose: To compare the mammographic malignant architectural distortion (AD) detection performance of radiologists who read mammographic examinations unaided versus those who read these examinations with the support of artificial intelligence (AI) systems.
Material And Methods: This retrospective case-control study was based on a double-reading of clinical mammograms between January 2011 and December 2016 at a large tertiary academic medical center. The study included 177 malignant and 90 benign architectural distortion (AD) patients.