Krishnan, Rahul G.

Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning

Chen, R. J., Chen, C., Li, Y., Chen, T. Y., Trister, A. D., Krishnan, R. G., & Mahmood, F. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 621

Deep Kalman Filters

Krishnan, R. G., Shalit, U., & Sontag, D. (2015) · arXiv

Gaussian Processes and Bayesian InferenceTarget Tracking and Data Fusion in Sensor Networks

Cited by 206

Performance of Large Language Models on Medical Oncology Examination Questions

Longwell, J. B., Hirsch, I., Binder, F., Gonzalez Conchas, G. A., Mau, D., Jang, R., Krishnan, R. G., & Grant, R. C. (2024) · JAMA Network Open

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 81

Self-Supervised Vision Transformers Learn Visual Concepts in Histopathology

Chen, R. J., & Krishnan, R. G. (2022) · arXiv

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 52

Clinical Camel: An Open Expert-Level Medical Language Model with Dialogue-Based Knowledge Encoding

Toma, A., Lawler, P. R., Ba, J., Krishnan, R. G., Rubin, B. B., & Wang, B. (2023) · arXiv

Artificial Intelligence in Healthcare and EducationTopic Modeling

Cited by 36

A Learning Based Hypothesis Test for Harmful Covariate Shift

Ginsberg, T., Liang, Z., & Krishnan, R. G. (2022) · arXiv

Adversarial Robustness in Machine LearningDomain Adaptation and Few-Shot Learning

Cited by 4