Prasanna, Prateek

Federated learning enables big data for rare cancer boundary detection

Pati, S., Baid, U., Edwards, B., Sheller, M., Wang, S.-H., Reina, G. A., Foley, P., Gruzdev, A., Karkada, D., Davatzikos, C., et al. (2022) · Nature Communications

Privacy-Preserving Technologies in DataBrain Tumor Detection and Classification

Cited by 394

Changes in CT Radiomic Features Associated with Lymphocyte Distribution Predict Overall Survival and Response to Immunotherapy in Non–Small Cell Lung Cancer

Khorrami, M., Prasanna, P., Gupta, A., Patil, P., Velu, P. D., Thawani, R., Corredor, G., Alilou, M., Bera, K., Fu, P., et al. (2020) · Cancer Immunology Research

Radiomics and Machine Learning in Medical ImagingCancer Immunotherapy and Biomarkers

Cited by 297

Radiogenomic analysis of hypoxia pathway is predictive of overall survival in Glioblastoma

Beig, N., Patel, J., Prasanna, P., Hill, V., Gupta, A., Correa, R., Bera, K., Singh, S., Partovi, S., Varadan, V., et al. (2018) · Scientific Reports

Radiomics and Machine Learning in Medical ImagingGlioma Diagnosis and Treatment

Cited by 157

CT derived radiomic score for predicting the added benefit of adjuvant chemotherapy following surgery in stage I, II resectable non-small cell lung cancer: a retrospective multicohort study for outcome prediction

Vaidya, P., Bera, K., Gupta, A., Wang, X., Corredor, G., Fu, P., Beig, N., Prasanna, P., Patil, P. D., Velu, P. D., et al. (2020) · The Lancet Digital Health

Radiomics and Machine Learning in Medical ImagingLung Cancer Diagnosis and Treatment

Cited by 134

Enhancing SAM with Efficient Prompting and Preference Optimization for Semi-supervised Medical Image Segmentation

Konwer, A., Yang, Z., Bas, E., Xiao, C., Prasanna, P., Bhatia, P., & Kass-Hout, T. (2025) · 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Medical Image Segmentation TechniquesDomain Adaptation and Few-Shot Learning

Cited by 17