Search Results for author: Boris Babenko

Found 9 papers, 1 papers with code

PathAlign: A vision-language model for whole slide images in histopathology

no code implementations27 Jun 2024 Faruk Ahmed, Andrew Sellergren, Lin Yang, Shawn Xu, Boris Babenko, Abbi Ward, Niels Olson, Arash Mohtashamian, Yossi Matias, Greg S. Corrado, Quang Duong, Dale R. Webster, Shravya Shetty, Daniel Golden, Yun Liu, David F. Steiner, Ellery Wulczyn

This enables applications utilizing a shared image-text embedding space, such as text or image retrieval for finding cases of interest, as well as integration of the WSI encoder with a frozen large language model (LLM) for WSI-based generative text capabilities such as report generation or AI-in-the-loop interactions.

Image Retrieval Language Modeling +5

Interdisciplinary Expertise to Advance Equitable Explainable AI

no code implementations29 May 2024 Chloe R. Bennett, Heather Cole-Lewis, Stephanie Farquhar, Naama Haamel, Boris Babenko, Oran Lang, Mat Fleck, Ilana Traynis, Charles Lau, Ivor Horn, Courtney Lyles

Interdisciplinary panel discussions can help reduce bias, identify potential confounders, and identify opportunities for additional research where there are gaps in the literature.

Epidemiology

Using generative AI to investigate medical imagery models and datasets

no code implementations1 Jun 2023 Oran Lang, Doron Yaya-Stupp, Ilana Traynis, Heather Cole-Lewis, Chloe R. Bennett, Courtney Lyles, Charles Lau, Michal Irani, Christopher Semturs, Dale R. Webster, Greg S. Corrado, Avinatan Hassidim, Yossi Matias, Yun Liu, Naama Hammel, Boris Babenko

In this paper, we present a method for automatic visual explanations leveraging team-based expertise by generating hypotheses of what visual signals in the images are correlated with the task.

scientific discovery

Detecting hidden signs of diabetes in external eye photographs

no code implementations23 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 Progression of Age-related Macular Degeneration from Fundus Images using Deep Learning

no code implementations10 Apr 2019 Boris Babenko, Siva Balasubramanian, Katy E. Blumer, Greg S. Corrado, Lily Peng, Dale R. Webster, Naama Hammel, Avinash V. Varadarajan

For predicting progression specifically from iAMD, the DL algorithm's sensitivity (57+/-6%) was also higher compared to the 9-step grades (36+/-8%) and the 4-category grades (20+/-0%).

Specificity

Poverty Mapping Using Convolutional Neural Networks Trained on High and Medium Resolution Satellite Images, With an Application in Mexico

no code implementations16 Nov 2017 Boris Babenko, Jonathan Hersh, David Newhouse, Anusha Ramakrishnan, Tom Swartz

We find that 1) the best models, which incorporate satellite-estimated land use as a predictor, explain approximately 57% of the variation in poverty in a validation sample of 10 percent of MCS-ENIGH municipalities; 2) Across all MCS-ENIGH municipalities explanatory power reduces to 44% in a CNN prediction and landcover model; 3) Predicted poverty from the CNN predictions alone explains 47% of the variation in poverty in the validation sample, and 37% over all MCS-ENIGH municipalities; 4) In urban areas we see slight improvements from using Digital Globe versus Planet imagery, which explain 61% and 54% of poverty variation respectively.

Cannot find the paper you are looking for? You can Submit a new open access paper.