Dr. Yading Yuan Profile

Dr. Yading Yuan

Associate Professor at Columbia Univ Irving Medical Ctr

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Publications (8)

Proceedings Article | 3 April 2024 Presentation + Paper

Decentralized gossip mutual learning (GML) for automatic head and neck tumor segmentation

Jingyun Chen, Yading Yuan

Proceedings Volume 12927, 129270T (2024) https://doi.org/10.1117/12.3005042

KEYWORDS: Machine learning, Tumors, Image segmentation, Neck, Head

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Proceedings Article | 2 April 2024 Presentation + Paper

FeSEC: a secure and efficient federated learning framework for medical imaging

Muhammad Asad, Yading Yuan

Proceedings Volume 12931, 129310F (2024) https://doi.org/10.1117/12.3005993

KEYWORDS: Data modeling, COVID 19, Data privacy, Medical imaging, Machine learning

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Proceedings Article | 4 April 2022 Presentation + Paper

Predicting 3D dose distribution with scale attention network for prostate cancer radiotherapy

Saba Adabi, Tzu-Chi Tzeng, Yading Yuan

Proceedings Volume 12034, 1203417 (2022) https://doi.org/10.1117/12.2611769

KEYWORDS: Radiotherapy, Data modeling, Prostate cancer, Network architectures, Binary data, 3D modeling, Computer programming, Computed tomography, Bladder, Machine learning

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Proceedings Article | 9 March 2010 Paper

Effect of variable gain on computerized texture analysis on digitized mammograms

Hui Li, Maryellen Giger, Li Lan, Yading Yuan, Neha Bhooshan, Olufunmilayo Olopade

Proceedings Volume 7624, 76242C (2010) https://doi.org/10.1117/12.845321

KEYWORDS: Breast cancer, Mammography, Breast, Fractal analysis, Feature extraction, Image analysis, Image processing, Scanners, Digital imaging, Receivers

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Proceedings Article | 3 March 2009 Paper

Computerized breast parenchymal analysis on DCE-MRI

Hui Li, Maryellen Giger, Yading Yuan, Sanaz Jansen, Li Lan, Neha Bhooshan, Gillian Newstead

Proceedings Volume 7260, 72600N (2009) https://doi.org/10.1117/12.813567

KEYWORDS: Breast, Breast cancer, Magnetic resonance imaging, Image segmentation, Fuzzy logic, Lithium, Cancer, Medical imaging, 3D image processing, Mammography

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Proceedings Article | 3 March 2009 Paper

Breast cancer classification with mammography and DCE-MRI

Yading Yuan, Maryellen Giger, Hui Li, Charlene Sennett

Proceedings Volume 7260, 72600O (2009) https://doi.org/10.1117/12.813723

KEYWORDS: Mammography, Feature selection, Breast cancer, Image segmentation, Magnetic resonance imaging, Databases, Medical imaging, Computer aided diagnosis and therapy, Current controlled current source, Diagnostics

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Since different imaging modalities provide complementary information regarding the same lesion, combining information from different modalities may increase diagnostic accuracy. In this study, we investigated the use of computerized features of lesions imaged via both full-field digital mammography (FFDM) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in the classification of breast lesions. Using a manually identified lesion location, i.e. a seed point on FFDM images or a ROI on DCE-MRI images, the computer automatically segmented mass lesions and extracted a set of features for each lesion. Linear stepwise feature selection was firstly performed on single modality, yielding one feature subset for each modality. Then, these selected features served as the input to another feature selection procedure when extracting useful information from both modalities. The selected features were merged by linear discriminant analysis (LDA) into a discriminant score. Receiver operating characteristic (ROC) analysis was used to evaluate the performance of the selected feature subset in the task of distinguishing between malignant and benign lesions. From a FFDM database with 321 lesions (167 malignant and 154 benign), and a DCE-MRI database including 181 lesions (97 malignant and 84 benign), we constructed a multi-modality dataset with 51 lesions (29 malignant and 22 benign). With leave-one-out-by-lesion evaluation on the multi-modality dataset, the mammography-only features yielded an area under the ROC curve (AUC) of 0.62 ± 0.08 and the DCE-MRI-only features yielded an AUC of 0.94±0.03. The combination of these two modalities, which included a spiculation feature from mammography and a kinetic feature from DCE-MRI, yielded an AUC of 0.94. The improvement of combining multi-modality information was statistically significant as compared to the use of mammography only (p=0.0001). However, we failed to show the statistically significant improvement as compared to DCE-MRI, with the limited multi-modality dataset (p=0.22).

Proceedings Article | 17 March 2008 Paper

Correlative feature analysis of FFDM images

Yading Yuan, Maryellen Giger, Hui Li, Charlene Sennett

Proceedings Volume 6915, 69151L (2008) https://doi.org/10.1117/12.770524

KEYWORDS: Image analysis, Statistical analysis, Image segmentation, Medical imaging, Computer aided diagnosis and therapy, Diagnostics, CAD systems, Breast, Mammography, Tissues

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Proceedings Article | 15 March 2006 Paper

A two-stage method for lesion segmentation on digital mammograms

Yading Yuan, Maryellen Giger, Kenji Suzuki, Hui Li, Andrew Jamieson

Proceedings Volume 6144, 61443W (2006) https://doi.org/10.1117/12.652840

KEYWORDS: Image segmentation, Mammography, Algorithm development, Tumors, Gradient-index optics, Computer aided diagnosis and therapy, Fuzzy logic, Lithium, Breast, Medical imaging

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Showing 5 of 8 publications

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