58 citations · 215 across the 11 of their papers we have counts for
12 papers
Gemma 4 Technical Report
Gemma Team, Sherif El Abd, Vaibhav Aggarwal +320
We introduce Gemma 4, a new generation of open-weight, natively multimodal language models in the Gemma model family. Designed to advance compute efficiency and reasoning, the Gemm…
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…
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…
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…
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…
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…
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…
Synthetically Enhanced: Unveiling Synthetic Data's Potential in Medical Imaging Research
Bardia Khosravi, Frank Li, Theo Dapamede +8
Chest X-rays (CXR) are essential for diagnosing a variety of conditions, but when used on new populations, model generalizability issues limit their efficacy. Generative AI, partic…
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…
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…