Dr. E. Shelley Hwang Profile

Dr. E. Shelley Hwang

at Duke Univ

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Author

Publications (8)

Proceedings Article | 23 March 2020 Presentation + Paper

A multitask deep learning method in simultaneously predicting occult invasive disease in ductal carcinoma in-situ and segmenting microcalcifications in mammography

Rui Hou, Lars Grimm, Maciej Mazurowski, Jeffrey Marks, Lorraine King, Carlo Maley, E. Shelley Hwang, Joseph Lo

Proceedings Volume 11314, 1131405 (2020) https://doi.org/10.1117/12.2549669

KEYWORDS: Image segmentation, Cancer, Breast, Breast cancer, Mammography

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Proceedings Article | 16 March 2020 Paper

Microcalcification localization and cluster detection using unsupervised convolutional autoencoders and structural similarity index

Yifan Peng, Rui Hou, Yinhao Ren, Lars Grimm, Jeffrey Marks, E. Shelley Hwang, Joseph Lo

Proceedings Volume 11314, 1131403 (2020) https://doi.org/10.1117/12.2551263

KEYWORDS: Mammography, Detection and tracking algorithms, Breast cancer, Reconstruction algorithms, Radiology, Breast, Artificial neural networks, Convolutional neural networks, Computer vision technology, Medical image processing

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Proceedings Article | 13 March 2019 Paper

Malignant microcalcification clusters detection using unsupervised deep autoencoders

Rui Hou, Yinhao Ren, Lars Grimm, Maciej Mazurowski, Jeffrey Marks, Lorraine King, Carlo Maley, E. Shelley Hwang, Joseph Lo

Proceedings Volume 10950, 109502Q (2019) https://doi.org/10.1117/12.2512829

KEYWORDS: Mammography, Cancer, Machine learning, Breast, Breast cancer, Radiology, Data modeling, Feature extraction

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Proceedings Article | 27 February 2018 Paper

Learning better deep features for the prediction of occult invasive disease in ductal carcinoma in situ through transfer learning

Bibo Shi, Rui Hou, Maciej Mazurowski, Lars Grimm, Yinhao Ren, Jeffrey Marks, Lorraine King, Carlo Maley, E. Shelley Hwang, Joseph Lo

Proceedings Volume 10575, 105752R (2018) https://doi.org/10.1117/12.2293594

KEYWORDS: Machine learning, Feature extraction, Feature selection, Breast cancer, Mammography, Image classification, Biopsy

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Proceedings Article | 27 February 2018 Paper

Deep learning-based features of breast MRI for prediction of occult invasive disease following a diagnosis of ductal carcinoma in situ: preliminary data

Zhe Zhu, Michael Harowicz, Jun Zhang, Ashirbani Saha, Lars Grimm, Shelley Hwang, Maciej Mazurowski

Proceedings Volume 10575, 105752W (2018) https://doi.org/10.1117/12.2295470

KEYWORDS: Magnetic resonance imaging, Medical imaging, Data modeling, Tumor growth modeling, Convolution, Breast, Neural networks, Cancer, Spatial resolution, Feature extraction

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Proceedings Article | 27 February 2018 Presentation + Paper

Improving classification with forced labeling of other related classes: application to prediction of upstaged ductal carcinoma in situ using mammographic features

Rui Hou, Bibo Shi, Lars Grimm, Maciej Mazurowski, Jeffrey Marks, Lorraine King, Carlo Maley, E. Shelley Hwang, Joseph Lo

Proceedings Volume 10575, 105750R (2018) https://doi.org/10.1117/12.2293809

KEYWORDS: Computer aided diagnosis and therapy, Cancer, Breast cancer, Surgery, Mammography, Feature extraction, Performance modeling, Breast, Biopsy, Radiology

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Proceedings Article | 3 March 2017 Presentation + Paper

Prediction of occult invasive disease in ductal carcinoma in situ using computer-extracted mammographic features

Bibo Shi, Lars Grimm, Maciej Mazurowski, Jeffrey Marks, Lorraine King, Carlo Maley, E. Shelley Hwang, Joseph Lo

Proceedings Volume 10134, 101341I (2017) https://doi.org/10.1117/12.2255731

KEYWORDS: CAD systems, Digital mammography, Breast cancer, Breast, Feature extraction, Feature selection, Machine vision, Computer vision technology, Performance modeling, Tumor growth modeling

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Proceedings Article | 3 March 2017 Paper

Can upstaging of ductal carcinoma in situ be predicted at biopsy by histologic and mammographic features?

Bibo Shi, Lars Grimm, Maciej Mazurowski, Jeffrey Marks, Lorraine King, Carlo Maley, E. Shelley Hwang, Joseph Lo

Proceedings Volume 10134, 101342X (2017) https://doi.org/10.1117/12.2255847

KEYWORDS: Statistical analysis, Digital mammography, Breast cancer, Feature extraction, Core needle biopsy, Performance modeling, Data modeling

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Showing 5 of 8 publications

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