452 citations · 455 across the 6 of their papers we have counts for
6 papers
V-REX: Efficient Specialist VLM Training for Veterinary X-Rays
Tim Elsner, Nicole McNally, Andre Dourson +1
While generalist VLMs are expensive to train, creating domain experts is widely assumed to require fine-tuning increasingly large foundation models. We show that, in veterinary rad…
VET-DINO: Learning Anatomical Understanding Through Multi-View Distillation in Veterinary Imaging
Andre Dourson, Kylie Taylor, Xiaoli Qiao +1
Self-supervised learning has emerged as a powerful paradigm for training deep neural networks, particularly in medical imaging where labeled data is scarce. While current approache…
PulseNet: Deep Learning ECG-signal classification using random augmentation policy and continous wavelet transform for canines
Andre Dourson, Roberto Santilli, Federica Marchesotti +8
Evaluating canine electrocardiograms (ECG) require skilled veterinarians, but current availability of veterinary cardiologists for ECG interpretation and diagnostic support is limi…
StudyFormer : Attention-Based and Dynamic Multi View Classifier for X-ray images
Lucas Wannenmacher, Michael Fitzke, Diane Wilson +1
Chest X-ray images are commonly used in medical diagnosis, and AI models have been developed to assist with the interpretation of these images. However, many of these models rely o…
MONAI: An open-source framework for deep learning in healthcare
M. Jorge Cardoso, Wenqi Li, Richard Brown +53
Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagn…
RapidRead: Global Deployment of State-of-the-art Radiology AI for a Large Veterinary Teleradiology Practice
Michael Fitzke, Conrad Stack, Andre Dourson +7
This work describes the development and real-world deployment of a deep learning-based AI system for evaluating canine and feline radiographs across a broad range of findings and a…