Vanguri, Rami

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Bakas, S., Reyes, M., Jakab, A., Bauer, S., Rempfler, M., Crimi, A., Shinohara, R. T., Berger, C., Ha, S. M., Rozycki, M., et al. (2018) · Apollo - University of Cambridge Repository

Brain Tumor Detection and ClassificationMedical Image Segmentation Techniques

Cited by 1,441

Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the brats challenge

Bakas, S., Reyes, M., Jakab, A., Bauer, S., Rempfler, M., Crimi, A., Shinohara, R. T., Berger, C., Ha, S. M., Rozycki, M., et al. (2018) · arXiv

Brain Tumor Detection and ClassificationMedical Image Segmentation Techniques

Cited by 1,441

Harnessing multimodal data integration to advance precision oncology

Boehm, K. M., Khosravi, P., Vanguri, R., Gao, J., & Shah, S. P. (2022) · Nature Reviews Cancer

Radiomics and Machine Learning in Medical ImagingArtificial Intelligence in Healthcare and Education

Cited by 662

Multimodal data integration using machine learning improves risk stratification of high-grade serous ovarian cancer

Boehm, K. M., Aherne, E. A., Ellenson, L., Nikolovski, I., Alghamdi, M., Vázquez-García, I., Zamarin, D., Long Roche, K., Liu, Y., Patel, D., et al. (2022) · Nature Cancer

Ovarian cancer diagnosis and treatmentRadiomics and Machine Learning in Medical Imaging

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A curated and standardized adverse drug event resource to accelerate drug safety research

Banda, J. M., Evans, L., Vanguri, R. S., Tatonetti, N. P., Ryan, P. B., & Shah, N. H. (2016) · Scientific Data

Pharmacovigilance and Adverse Drug ReactionsData-Driven Disease Surveillance

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Automated classification of cellular expression in multiplexed imaging data with Nimbus

Rumberger, J. L., Greenwald, N. F., Ranek, J. S., Boonrat, P., Walker, C., Franzen, J., Varra, S. R., Kong, A., Sowers, C., Liu, C. C., et al. (2024) · Nature Methods

Cell Image Analysis TechniquesSingle-cell and spatial transcriptomics

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Weakly Supervised Deep Learning Predicts Immunotherapy Response in Solid Tumors Based on PD-L1 Expression

Ligero, M., Serna, G., El Nahhas, O. S. M., Sansano, I., Mauchanski, S., Viaplana, C., Calderaro, J., Toledo, R. A., Dienstmann, R., Vanguri, R. S., et al. (2024) · Cancer Research Communications

AI in cancer detectionCancer Immunotherapy and Biomarkers

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