Joseph Bae
Education:
B.S. University of Southern California (2018)
M.S. University of Southern California (2018)
Ph.D. Stony Brook University (2025)
Advisor:
Prateek Prasanna
Graduate Program:
Biomedical Informatics
Research Interest:
I am broadly interested in applying computational and physical science approaches to clinically relevant questions in cancer. My previous research involved translational studies of circulating tumor cells (CTCs) in patients with solid tumors, primarily examining the effects of anti-cancer therapies on genotypic and phenotypic diversity within CTC populations.
My Ph.D. thesis focused on computational approaches for modeling multimodal imaging and treatment-planning data from patients with breast, head and neck, brain, and lung cancers. This work investigated whether imaging-derived information could help characterize or predict patient response to treatment. A major additional focus was identifying bias in artificial intelligence datasets/methods and developing approaches to mitigate these biases.
I currently conduct research with Dr. Peng Jiang at the National Cancer Institute, where I leverage computational methods to study gene expression and adaptive immune receptor repertoire data with applications in cellular cancer immunotherapy.
You can read more about my work at https://joseph-bae.github.io/.
Publications:
Journal Papers
Wu, J.; Bae, J.; Chen, C.; Ryu, S.; Lozeau, D.; Stessin, A.; & Prasanna, P. (2025). Magnetic Resonance Imaging Radiomic Analysis of Radiation-Induced Morphea of the Breast: A Proof-of-Concept Study. Advances in Radiation Oncology.
Bae, J.; Mani, K.; Czerwonka, L; Vanison, C; Ryu, S.; & Prasanna, P. (2025). Spatial Radiomic Graphs for Outcome Prediction in Radiotherapy-Treated Head and Neck Squamous Cell Carcinoma Using Pre-Treatment CT. Radiology: Imaging Cancer.
Singh, G.; Singh, A.; Bae, J.; Manjila, S.; Spektor, V.; Prasanna, P.; & Lignelli, A. (2024). New frontiers in domain-inspired radiomics and radiogenomics: increasing role of molecular diagnostics in CNS tumor classification and grading following WHO CNS-5 updates. Cancer Imaging.
Bae, J.; Mani, K.; Zabrocka, E.; Cattell, R.; O’Grady, B.; Payne, D.; Roberson, J.; Ryu, S.; & Prasanna, P. (2024). Pre-treatment Spatially-Aware MRI Radiomics Can Predict Distant Brain Metastases (DBMs) Following Stereotactic Radiosurgery/Radiation Therapy (SRS/SRT). Advances in Radiation Oncology.
Bae, J.; Kapse, S.; Singh, G.; Gattu, R.; Ali, S.; Shah, N.; Marshall, C.; Pierce, J.; Phatak, T.; Gupta, A.; Green, J.; Madan, N.; Prasanna, P. (2021) Predicting Mechanical Ventilation and Mortality in COVID-19 Using Radiomics and Deep Learning on Chest Radiographs: A Multi-Institutional Study. Diagnostics.
Khullar, R.; Shah, S.; Singh, G.; Bae, J.; Gattu, R.; Jain, S.; Green, J.; Anandarangam, T.; Cohen, M.; Madan, N.; & Prasanna, P. (2020). Effects of Prone Ventilation on Oxygenation, Inflammation, and Lung Infiltrates in COVID-19 Related Acute Respiratory Distress Syndrome: A Retrospective Cohort Study. Journal of Clinical Medicine.
Conference Papers
Bae, J.; Kapse, S.; Zhou, L.; Mani, K; & Prasanna, P. (2024). HoG-Net: Hierarchical Multi-organ Graph Network for Head and Neck Cancer Recurrence Prediction from CT Images. MICCAI 2024.
Bae, J.; Guo, X; Yerebakan, H; Shinagawa, Y; & Farhand, S. (2024). SAMU: An Efficient and Promptable Foundation Model for Medical Image Segmentation. MEDAGI@MICCAI 2024.
Zhou, L.; Liu, H.; Bae, J.; Samaras, D.; Prasanna, P. (2023). Self Pre-training with Masked Autoencoders for Medical Image Classification and Segmentation. ISBI 2023
Konwer, A.; Hu, X.; Bae, J.; Xu, X.; Chen, C.; Prasanna, P. (2023). Enhancing Modality-Agnostic Representations via Meta-Learning for Brain Tumor Segmentation. ICCV 2023
Zhou, L.; Liu, H.; Bae, J.; He, J.; Samaras, D.; Prasanna, P. (2023). Token Sparsification for Faster Medical Image Segmentation. IPMI 2023
Bae, J.; Cattell, R.; Zabrocka, E.; Roberson, J.; Payne, D.; Mani, K.; Prasanna, P. Pre-Treatment Radiomics from Radiotherapy Dose Regions Predict Distant Brain Metastases in Stereotactic Radiosurgery. In Medical Imaging 2022: Physics of Medical Imaging; SPIE, 2022.
Konwer, A.; Xu, X.; Bae, J.; Chen, C.; Prasanna, P. Temporal Context Matters: Enhancing Single Image Prediction With Disease Progression Representations. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2022.
Konwer, A.; Bae, J.; Singh, G.; Gattu, R.; Ali, S.; Green, J.; Phatak, T.; Gupta, A.; Chen, C.; Saltz, J.; Prasanna, P. Predicting COVID-19 Lung Infiltrate Progression on Chest Radiographs Using Spatio-Temporal LSTM Based Encoder-Decoder Network; Medical Vision with Deep Learning 2021.
Zhou, L.; Bae, J.; Liu, H.; Singh, G.; Green, J.; Samaras, D.; Prasanna, P. Chest Radiograph Disentanglement for COVID-19 Outcome Prediction. In Medical Image Computing and Computer Assisted Intervention – MICCAI 2021.
Konwer, A.; Bae, J.; Singh, G.; Gattu, R.; Ali, S.; Green, J.; Phatak, T.; Prasanna, P. Attention-Based Multi-Scale Gated Recurrent Encoder with Novel Correlation Loss for COVID-19 Progression Prediction. In Medical Image Computing and Computer Assisted Intervention – MICCAI 2021.
Cowan, C.; Bae, J.; Singh, G.; Khullar, R.; Shah, S.; Madan, N.; Prasanna, P. Evolution of Chest Radiograph Radiomics and Association with Respiratory and Inflammatory Parameters in COVID-19 Patients Undergoing Prone Ventilation: Preliminary Findings; International Society for Optics and Photonics 2021. Oral Presentation
Conference Posters and Oral Presentations:
Bae, J. & Cha, B. (2025). Identifying Age, Race, and Sex Biases in Public Imaging and Genomic Datasets for Radiotherapy Patients. ASTRO 2025.
Bae, J.; Torre-Healy, L.; Wu, J.; Pang, L.; Stessin, A.; Hsia, A.T.; Farrel, R.; Mani, K.M.; Prassan, P.; & Ryu, S. (2025). Stereotactic Radiosurgery (SRS) over Whole Brain Radiotherapy (WBRT) Preserves Brain Volume: An Imaging and Radiomic Analysis. ASTRO 2025.
Bae, J.; Mani, K.; Noldner, C.; Czerwonka, L.; Ryu, S.; & Prasanna, P. (2024). Do Spatial-Radiomics Improve Prediction of Locoregional Recurrence Following Radiotherapy for HNSCC? Multidisciplinary Head and Neck Cancers Symposium 2024.
Bae, J. & Prasanna, P. (2023). Graph-Based Spatially-Aware Radiomics Improves Prediction of Locoregional Recurrence in Radiotherapy-Treated Head and Neck Squamous Cell Carcinoma. RSNA 2023 Oral Presentation (Trainee Research Prize)
Bae, J; Mani, K.; Zabrocka, E.; Cattel, R.; O’Grady, B.; Payne, D.; Roberson, J.; Ryu, S.; Prasanna, P. (2023). Predictive Value of Pre-Treatment MRI Radiomics for Distant Brain Metastases Following Stereotactic Radiosurgery/Radiotherapy. ASTRO 2023.
Bae, J.; Prasanna, P.; Gadgeel, S. (2022). Pre-treatment CT Radiomics Predicts Survival in Chemo-Immunotherapy-treated Small Cell Lung Cancer. ESMO 2022
Noldner, C.; Bae, J.; Kartsonis, W.; Cattell, R.; Soft, S.; Sehgal, G.; Pierce, A.; Patel, M.; Ryu, S.; Czerwonka, L.; Prasanna, P.; Mani, K. Pre-Radiation CT-based Radiomic Features Predict Locoregional and Distant Failure in Locally Advanced Head and Neck Cancer. Multidisciplinary Thoracic Cancer Symposium 2022.
Bae, J.; Zabrocka, E.; Rodriguez, C.; Payne, D.; Ryu, S.; Prasanna, P.; Mani, K. Prediction of Regional and Distant Failure after Definitive Thoracic Stereotactic Body Radiation Therapy Using Pre-Treatment CT-Based Radiomic Analysis, Multidisciplinary Thoracic Cancer Symposium 2021.