JB
Joseph Bae
National Cancer Institute, Stony Brook University, University of Southern California, Siemens Healthcare (United States)
Research areas: Medicine · Radiology, Nuclear Medicine and Imaging · Radiomics and Machine Learning in Medical Imaging · COVID-19 Clinical Research Studies · COVID-19 diagnosis using AI
0000-0002-9440-6655
Employment
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National Cancer Institute Research Fellow/Rotating Medical Student2026 - Present
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Siemens Healthineers USA Image/Text Analytics Intern2024 - 2024
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Stony Brook University MD/PhD Student2019 - Present
Education
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Stony Brook University Ph.D. Biomedical Informatics2021 - 2025
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Stony Brook University M.D. Doctor of Medicine2019 - 2027
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University of Southern California M.S. Medical Biophysics2016 - 2018
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University of Southern California B.S. Biophysics2014 - 2018
Projects & Funding
Projects & funding information is unavailable.
Publications (31)
- Identifying Age, Race, and Sex Biases in Public Imaging and Genomic Datasets for Radiotherapy Patients Save
- Magnetic Resonance Imaging Radiomic Analysis of Radiation-Induced Morphea of the Breast: A Proof-of-Concept Study Save
- Spatial radiomic graphs for outcome prediction in radiation therapy--treated head and neck squamous cell carcinoma using pretreatment ct Save
- Stereotactic Radiosurgery (SRS) over Whole Brain Radiotherapy (WBRT) Preserves Brain Volume: An Imaging and Radiomic Analysis Save
- Do spatial-radiomics improve prediction of locoregional recurrence following radiotherapy for hnscc? Save
- Hog-net: hierarchical multi-organ graph network for head and neck cancer recurrence prediction from CT images Save
- New frontiers in domain-inspired radiomics and radiogenomics: increasing role of molecular diagnostics in CNS tumor classification and grading following WHO CNS-5 updates Save
- Pre-treatment spatially-aware mri radiomics can predict distant brain metastases (dbms) following stereotactic radiosurgery/radiation therapy (srs/srt) Save
- Samu: An efficient and promptable foundation model for medical image segmentation Save
- Trainee Research Prizes from the 2023 RSNA Scientific Assembly and Annual Meeting Save
- Enhancing modality-agnostic representations via meta-learning for brain tumor segmentation Save
- INRFormer: Neuron Permutation Equivariant Transformer on Implicit Neural Representations Save
- Predictive Value of Pre-Treatment MRI Radiomics for Distant Brain Metastases Following Stereotactic Radiosurgery/Radiotherapy Save
- Self pre-training with masked autoencoders for medical image classification and segmentation Save
- Token sparsification for faster medical image segmentation Save
- 1544P Pre-treatment CT radiomics predicts survival in chemo-immunotherapy-treated small cell lung cancer Save
- Pre-Radiation CT-Based Radiomic Features Predict Locoregional and Distant Failure in Locally Advanced Head and Neck Cancer Save
- Pre-treatment radiomics from radiotherapy dose regions predict distant brain metastases in stereotactic radiosurgery Save
- Prediction of Regional and Distant Failure After Definitive Thoracic Stereotactic Body Radiation Therapy (SBRT) Using Pre-Treatment CT-Based Radiomic Analysis Save
- Temporal context matters: Enhancing single image prediction with disease progression representations Save