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Peter D. Chang
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2020 – today
- 2024
- [i2]Peter D. Chang:
DeepATLAS: One-Shot Localization for Biomedical Data. CoRR abs/2402.09587 (2024) - [i1]Jeffrey D. Rudie, Hui-Ming Lin, Robyn L. Ball, Sabeena Jalal, Luciano M. Prevedello, Savvas Nicolaou, Brett S. Marinelli, Adam E. Flanders, Kirti Magudia, George Shih, Melissa A. Davis, John Mongan, Peter D. Chang, Ferco H. Berger, Sebastiaan Hermans, Meng Law, Tyler Richards, Jan-Peter Grunz, Andreas Steven Kunz, Shobhit Mathur, Sandro Galea-Soler, Andrew D. Chung, Saif Afat, Chin-Chi Kuo, Layal Aweidah, Ana Villanueva Campos, Arjuna Somasundaram, Felipe Antonio Sanchez Tijmes, Attaporn Jantarangkoon, Leonardo Kayat Bittencourt, Michael Brassil, Ayoub El Hajjami, Hakan Dogan, Muris Becircic, Agrahara G. Bharatkumar, Eduardo Moreno Júdice de Mattos Farina, Dataset Curator Group, Dataset Contributor Group, Dataset Annotator Group, Errol Colak:
The RSNA Abdominal Traumatic Injury CT (RATIC) Dataset. CoRR abs/2405.19595 (2024) - 2021
- [j9]Su Kara
, Jake Y. Akers, Peter D. Chang:
Identification and Localization of Endotracheal Tube on Chest Radiographs Using a Cascaded Convolutional Neural Network Approach. J. Digit. Imaging 34(4): 898-904 (2021) - 2020
- [c2]Yeeleng Scott Vang, Yingxin Cao, Peter D. Chang, Daniel S. Chow, Alexander Ulrich Brandt, Friedemann Paul, Michael Scheel, Xiaohui Xie:
SynergyNet: A Fusion Framework for Multiple Sclerosis Brain MRI Segmentation with Local Refinement. ISBI 2020: 131-135
2010 – 2019
- 2019
- [j8]Richard Ha
Fully Automated Convolutional Neural Network Method for Quantification of Breast MRI Fibroglandular Tissue and Background Parenchymal Enhancement. J. Digit. Imaging 32(1): 141-147 (2019) - [j7]Richard Ha
Predicting Breast Cancer Molecular Subtype with MRI Dataset Utilizing Convolutional Neural Network Algorithm. J. Digit. Imaging 32(2): 276-282 (2019) - [j6]Joseph N. Stember
Eye Tracking for Deep Learning Segmentation Using Convolutional Neural Networks. J. Digit. Imaging 32(4): 597-604 (2019) - [j5]Richard Ha
Prior to Initiation of Chemotherapy, Can We Predict Breast Tumor Response? Deep Learning Convolutional Neural Networks Approach Using a Breast MRI Tumor Dataset. J. Digit. Imaging 32(5): 693-701 (2019) - [j4]Joseph N. Stember
Convolutional Neural Networks for the Detection and Measurement of Cerebral Aneurysms on Magnetic Resonance Angiography. J. Digit. Imaging 32(5): 808-815 (2019) - [j3]Peter D. Chang, Tony T. Wong, Michael J. Rasiej
Deep Learning for Detection of Complete Anterior Cruciate Ligament Tear. J. Digit. Imaging 32(6): 980-986 (2019) - 2018
- [j2]Simukayi Mutasa
MABAL: a Novel Deep-Learning Architecture for Machine-Assisted Bone Age Labeling. J. Digit. Imaging 31(4): 513-519 (2018) - [j1]Richard Ha
Axillary Lymph Node Evaluation Utilizing Convolutional Neural Networks Using MRI Dataset. J. Digit. Imaging 31(6): 851-856 (2018) - 2016
- [c1]Peter D. Chang:
Fully Convolutional Deep Residual Neural Networks for Brain Tumor Segmentation. BrainLes@MICCAI 2016: 108-118
Coauthor Index
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