Ramasamy, Kim

Underspecification Presents Challenges for Credibility in Modern Machine Learning

D'Amour, A., Heller, K., Moldovan, D., Adlam, B., Alipanahi, B., Beutel, A., Chen, C., Deaton, J., Eisenstein, J., Hoffman, M. D., et al. (2020) · arXiv

Domain Adaptation and Few-Shot LearningAdversarial Robustness in Machine Learning

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Performance of a Deep-Learning Algorithm vs Manual Grading for Detecting Diabetic Retinopathy in India

Gulshan, V., Rajan, R. P., Widner, K., Wu, D., Wubbels, P., Rhodes, T., Whitehouse, K., Coram, M., Corrado, G., Ramasamy, K., et al. (2019) · JAMA Ophthalmology

Retinal Imaging and AnalysisRetinal Diseases and Treatments

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Prevalence of diabetic retinopathy in India stratified by known and undiagnosed diabetes, urban–rural locations, and socioeconomic indices: results from the SMART India population-based cross-sectional screening study

Raman, R., Vasconcelos, J. C., Rajalakshmi, R., Prevost, A. T., Ramasamy, K., Mohan, V., Mohan, D., Rani, P. K., Conroy, D., Das, T., et al. (2022) · The Lancet Global Health

Retinal Diseases and TreatmentsRetinal Imaging and Analysis

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Developing a Screening Program to Detect Sight-Threatening Diabetic Retinopathy in South India

Namperumalsamy, P., Nirmalan, P. K., & Ramasamy, K. (2003) · Diabetes Care

Retinal Diseases and TreatmentsRetinal Imaging and Analysis

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