Eun-Sil Shelley Hwang
According to our database1,
Eun-Sil Shelley Hwang
authored at least 14 papers
between 2017 and 2022.
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Bibliography
2022
IEEE Trans. Biomed. Eng., 2022
DCIS AI-TIL: Ductal Carcinoma In Situ Tumour Infiltrating Lymphocyte Scoring Using Artificial Intelligence.
Proceedings of the Artificial Intelligence over Infrared Images for Medical Applications and Medical Image Assisted Biomarker Discovery, 2022
Automated Dcis Identification From Multiplex Immunohistochemistry Using Generative Adversarial Networks.
Proceedings of the 19th IEEE International Symposium on Biomedical Imaging, 2022
2021
A new method to accurately identify single nucleotide variants using small FFPE breast samples.
Briefings Bioinform., 2021
2020
Prediction of Upstaged Ductal Carcinoma In Situ Using Forced Labeling and Domain Adaptation.
IEEE Trans. Biomed. Eng., 2020
Microcalcification localization and cluster detection using unsupervised convolutional autoencoders and structural similarity index.
Proceedings of the Medical Imaging 2020: Computer-Aided Diagnosis, 2020
A multitask deep learning method in simultaneously predicting occult invasive disease in ductal carcinoma in-situ and segmenting microcalcifications in mammography.
Proceedings of the Medical Imaging 2020: Computer-Aided Diagnosis, 2020
2019
Deep learning analysis of breast MRIs for prediction of occult invasive disease in ductal carcinoma in situ.
Comput. Biol. Medicine, 2019
Malignant microcalcification clusters detection using unsupervised deep autoencoders.
Proceedings of the Medical Imaging 2019: Computer-Aided Diagnosis, 2019
2018
Deep learning-based features of breast MRI for prediction of occult invasive disease following a diagnosis of ductal carcinoma in situ: preliminary data.
Proceedings of the Medical Imaging 2018: Computer-Aided Diagnosis, 2018
Learning better deep features for the prediction of occult invasive disease in ductal carcinoma in situ through transfer learning.
Proceedings of the Medical Imaging 2018: Computer-Aided Diagnosis, 2018
Improving classification with forced labeling of other related classes: application to prediction of upstaged ductal carcinoma in situ using mammographic features.
Proceedings of the Medical Imaging 2018: Computer-Aided Diagnosis, 2018
2017
Can upstaging of ductal carcinoma in situ be predicted at biopsy by histologic and mammographic features?
Proceedings of the Medical Imaging 2017: Computer-Aided Diagnosis, 2017
Prediction of occult invasive disease in ductal carcinoma in situ using computer-extracted mammographic features.
Proceedings of the Medical Imaging 2017: Computer-Aided Diagnosis, 2017