activity
20212025
most citedAdvancing Multimodal Medical Capabilities of Gemini

30 citations · 100 across the 12 of their papers we have counts for

collaborators

13 papers

physics.geo-ph2025

Estimating high-resolution albedo for urban applications

David Fork, Elizabeth Jane Wesley, Salil Banerjee +18

Implementation of cool roofs is a high-impact pathway for mitigating heat at both global and city scales. However, while albedo estimates derived from Sentinel-2 are free and globa…

cs.CV2025★ 1 cited

PolyPath: Adapting a Large Multimodal Model for Multi-slide Pathology Report Generation

Faruk Ahmed, Lin Yang, Tiam Jaroensri +11

The interpretation of histopathology cases underlies many important diagnostic and treatment decisions in medicine. Notably, this process typically requires pathologists to integra…

cs.AI2024

Plots Unlock Time-Series Understanding in Multimodal Models

Mayank Daswani, Mathias M. J. Bellaiche, Marc Wilson +11

While multimodal foundation models can now natively work with data beyond text, they remain underutilized in analyzing the considerable amounts of multi-dimensional time-series dat…

cs.LG2024★ 3 cited

General Geospatial Inference with a Population Dynamics Foundation Model

Mohit Agarwal, Mimi Sun, Chaitanya Kamath +31

Supporting the health and well-being of dynamic populations around the world requires governmental agencies, organizations and researchers to understand and reason over complex rel…

cs.AI2024★ 13 cited

Towards a Personal Health Large Language Model

Justin Cosentino, Anastasiya Belyaeva, Xin Liu +31

In health, most large language model (LLM) research has focused on clinical tasks. However, mobile and wearable devices, which are rarely integrated into such tasks, provide rich,…

cs.CV2024★ 30 cited

Advancing Multimodal Medical Capabilities of Gemini

Lin Yang, Shawn Xu, Andrew Sellergren +44

Many clinical tasks require an understanding of specialized data, such as medical images and genomics, which is not typically found in general-purpose large multimodal models. Buil…

cs.LG2024★ 9 cited

HeAR -- Health Acoustic Representations

Sebastien Baur, Zaid Nabulsi, Wei-Hung Weng +15

Health acoustic sounds such as coughs and breaths are known to contain useful health signals with significant potential for monitoring health and disease, yet are underexplored in…

cs.LG2023

Optimizing Audio Augmentations for Contrastive Learning of Health-Related Acoustic Signals

Louis Blankemeier, Sebastien Baur, Wei-Hung Weng +5

Health-related acoustic signals, such as cough and breathing sounds, are relevant for medical diagnosis and continuous health monitoring. Most existing machine learning approaches…

cs.CV2023★ 18 cited

ELIXR: Towards a general purpose X-ray artificial intelligence system through alignment of large language models and radiology vision encoders

Shawn Xu, Lin Yang, Christopher Kelly +25

In this work, we present an approach, which we call Embeddings for Language/Image-aligned X-Rays, or ELIXR, that leverages a language-aligned image encoder combined or grafted onto…

cs.CV2023★ 1 cited

Predicting Cardiovascular Disease Risk using Photoplethysmography and Deep Learning

Wei-Hung Weng, Sebastien Baur, Mayank Daswani +13

Cardiovascular diseases (CVDs) are responsible for a large proportion of premature deaths in low- and middle-income countries. Early CVD detection and intervention is critical in t…

cs.CV2023★ 1 cited

Learning to Detect Touches on Cluttered Tables

Norberto Adrian Goussies, Kenji Hata, Shruthi Prabhakara +28

We present a novel self-contained camera-projector tabletop system with a lamp form-factor that brings digital intelligence to our tables. We propose a real-time, on-device, learni…

eess.IV2021★ 1 cited

Deep learning for detecting pulmonary tuberculosis via chest radiography: an international study across 10 countries

Sahar Kazemzadeh, Jin Yu, Shahar Jamshy +21

Tuberculosis (TB) is a top-10 cause of death worldwide. Though the WHO recommends chest radiographs (CXRs) for TB screening, the limited availability of CXR interpretation is a bar…

cs.CV2021★ 23 cited

Supervised Transfer Learning at Scale for Medical Imaging

Basil Mustafa, Aaron Loh, Jan Freyberg +12

Transfer learning is a standard technique to improve performance on tasks with limited data. However, for medical imaging, the value of transfer learning is less clear. This is lik…