Training Differentially Private Ad Prediction Models with Semi-Sensitive Features

no code implementations • 26 Jan 2024 • Lynn Chua, Qiliang Cui, Badih Ghazi, Charlie Harrison, Pritish Kamath, Walid Krichene, Ravi Kumar, Pasin Manurangsi, Krishna Giri Narra, Amer Sinha, Avinash Varadarajan, Chiyuan Zhang

Motivated by problems arising in digital advertising, we introduce the task of training differentially private (DP) machine learning models with semi-sensitive features.

Detecting hidden signs of diabetes in external eye photographs

no code implementations • 23 Nov 2020 • Boris Babenko, Akinori Mitani, Ilana Traynis, Naho Kitade, Preeti Singh, April Maa, Jorge Cuadros, Greg S. Corrado, Lily Peng, Dale R. Webster, Avinash Varadarajan, Naama Hammel, Yun Liu

In validation set A (n=27, 415 patients, all undilated), the DLS detected poor blood glucose control (HbA1c > 9%) with an area under receiver operating characteristic curve (AUC) of 70. 2; moderate-or-worse DR with an AUC of 75. 3; diabetic macular edema with an AUC of 78. 0; and vision-threatening DR with an AUC of 79. 4.

Management

Predicting optical coherence tomography-derived diabetic macular edema grades from fundus photographs using deep learning

no code implementations • 18 Oct 2018 • Avinash Varadarajan, Pinal Bavishi, Paisan Raumviboonsuk, Peranut Chotcomwongse, Subhashini Venugopalan, Arunachalam Narayanaswamy, Jorge Cuadros, Kuniyoshi Kanai, George Bresnick, Mongkol Tadarati, Sukhum Silpa-archa, Jirawut Limwattanayingyong, Variya Nganthavee, Joe Ledsam, Pearse A. Keane, Greg S. Corrado, Lily Peng, Dale R. Webster

To improve the accuracy of DME screening, we trained a deep learning model to use color fundus photographs to predict ci-DME.

Specificity