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Xuetong Tao
at The Univ of Sydney
SPIE Involvement:
Author
Publications (5)
SPIE Journal Paper | 3 October 2024
Optimizing mammography interpretation education: leveraging deep learning for cohort-specific error detection to enhance radiologist training
Xuetong Tao, Warren Reed, Tong Li, Patrick Brennan, Ziba Gandomkar
JMI, Vol. 11, Issue 05, 055502, (October 2024) https://doi.org/10.1117/12.10.1117/1.JMI.11.5.055502
KEYWORDS: Mammography, Machine learning, Education and training, Diagnostics, Deep learning, Performance modeling, Data modeling, Cross validation, Medical imaging, Image segmentation
Proceedings Article | 29 March 2024 Presentation + Paper
CNN-based transfer learning with 10-fold cross-validation: a novel approach for customized education of mammography training
Xuetong Tao, Ziba Gandomkar, Tong Li, Jessica Yi, Patrick Brennan, Warren Reed
Proceedings Volume 12929, 1292904 (2024) https://doi.org/10.1117/12.3006659
KEYWORDS: Mammography, Education and training, Performance modeling, Cross validation, Diagnostics, Breast cancer, Radiology, Data modeling, Breast
Proceedings Article | 3 April 2023 Presentation + Paper
False-negative diagnosis might occur due to absence of the global radiomic signature of malignancy on screening mammograms
Proceedings Volume 12467, 124670A (2023) https://doi.org/10.1117/12.2655154
KEYWORDS: Mammography, Cancer, Radiomics, Breast cancer, Cancer detection, Image classification, Breast, Artificial intelligence
Proceedings Article | 3 April 2023 Presentation + Paper
Investigating the error-making patterns in reading high-density screening mammograms between radiologists from two countries
Proceedings Volume 12467, 124670B (2023) https://doi.org/10.1117/12.2655164
KEYWORDS: Mammography, Cancer, Education and training, Breast density, Breast, Diagnostics, Cancer detection, Breast cancer, Biopsy, Radiology
Proceedings Article | 4 April 2022 Poster + Paper
Varying performance levels for diagnosing mammographic images depending on reader nationality have AI and educational implications
Xuetong Tao, Ziba Gandomkar, Tong Li, Warren Reed, Patrick Brennan
Proceedings Volume 12035, 1203517 (2022) https://doi.org/10.1117/12.2611342
KEYWORDS: Mammography, Cancer, Breast, Diagnostics, Breast cancer, Artificial intelligence, Imaging systems, Computer aided diagnosis and therapy
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