469 citations · 864 across the 21 of their papers we have counts for
23 papers
MedGemma 1.5 Technical Report
Andrew Sellergren, Chufan Gao, Fereshteh Mahvar +39
We introduce MedGemma 1.5 4B, the latest model in the MedGemma collection. MedGemma 1.5 expands on MedGemma 1 by integrating additional capabilities: high-dimensional medical imagi…
UWB Radar-based Heart Rate Monitoring: A Transfer Learning Approach
Elzbieta Gruzewska, Pooja Rao, Sebastien Baur +14
Radar technology presents untapped potential for continuous, contactless, and passive heart rate monitoring via consumer electronics like mobile phones. However the variety of avai…
MedGemma Technical Report
Andrew Sellergren, Sahar Kazemzadeh, Tiam Jaroensri +78
Artificial intelligence (AI) has significant potential in healthcare applications, but its training and deployment faces challenges due to healthcare's diverse data, complex tasks,…
CoCa-CXR: Contrastive Captioners Learn Strong Temporal Structures for Chest X-Ray Vision-Language Understanding
Yixiong Chen, Shawn Xu, Andrew Sellergren +6
Vision-language models have proven to be of great benefit for medical image analysis since they learn rich semantics from both images and reports. Prior efforts have focused on bet…
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…
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…
Health AI Developer Foundations
Atilla P. Kiraly, Sebastien Baur, Kenneth Philbrick +23
Robust medical Machine Learning (ML) models have the potential to revolutionize healthcare by accelerating clinical research, improving workflows and outcomes, and producing novel…
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…
Community search signatures as foundation features for human-centered geospatial modeling
Mimi Sun, Chaitanya Kamath, Mohit Agarwal +7
Aggregated relative search frequencies offer a unique composite signal reflecting people's habits, concerns, interests, intents, and general information needs, which are not found…
PathAlign: A vision-language model for whole slide images in histopathology
Faruk Ahmed, Andrew Sellergren, Lin Yang +14
Microscopic interpretation of histopathology images underlies many important diagnostic and treatment decisions. While advances in vision-language modeling raise new opportunities…
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,…
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…
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…
Consensus, dissensus and synergy between clinicians and specialist foundation models in radiology report generation
Ryutaro Tanno, David G. T. Barrett, Andrew Sellergren +23
Radiology reports are an instrumental part of modern medicine, informing key clinical decisions such as diagnosis and treatment. The worldwide shortage of radiologists, however, re…
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…
Multimodal LLMs for health grounded in individual-specific data
Anastasiya Belyaeva, Justin Cosentino, Farhad Hormozdiari +6
Foundation large language models (LLMs) have shown an impressive ability to solve tasks across a wide range of fields including health. To effectively solve personalized health tas…
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…
Understanding metric-related pitfalls in image analysis validation
Annika Reinke, Minu D. Tizabi, Michael Baumgartner +75
Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation int…
Metrics reloaded: Recommendations for image analysis validation
Lena Maier-Hein, Annika Reinke, Patrick Godau +71
Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen…
Enabling faster and more reliable sonographic assessment of gestational age through machine learning
Chace Lee, Angelica Willis, Christina Chen +8
Fetal ultrasounds are an essential part of prenatal care and can be used to estimate gestational age (GA). Accurate GA assessment is important for providing appropriate prenatal ca…
AI system for fetal ultrasound in low-resource settings
Ryan G. Gomes, Bellington Vwalika, Chace Lee +26
Despite considerable progress in maternal healthcare, maternal and perinatal deaths remain high in low-to-middle income countries. Fetal ultrasound is an important component of ant…
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…
Deep Learning for Distinguishing Normal versus Abnormal Chest Radiographs and Generalization to Unseen Diseases
Zaid Nabulsi, Andrew Sellergren, Shahar Jamshy +20
Chest radiography (CXR) is the most widely-used thoracic clinical imaging modality and is crucial for guiding the management of cardiothoracic conditions. The detection of specific…