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Found 5 papers, 2 papers with code
VISION-MAE: A Foundation Model for Medical Image Segmentation and Classification
A foundation model, which is a large-scale pre-trained AI model, offers a versatile base that can be adapted to a variety of specific tasks and contexts.
MRAnnotator: A Multi-Anatomy Deep Learning Model for MRI Segmentation
The external dataset was used to evaluate nnU-Net model generalizability and performance in all classes on independent imaging data.
RadImageGAN -- A Multi-modal Dataset-Scale Generative AI for Medical Imaging
We showed that using synthetic auto-labeled data from RadImageGAN can significantly improve performance on four diverse downstream segmentation datasets by augmenting real training data and/or developing pre-trained weights for fine-tuning.
Generative AI for Medical Imaging: extending the MONAI Framework
We have implemented these models in a generalisable fashion, illustrating that their results can be extended to 2D or 3D scenarios, including medical images with different modalities (like CT, MRI, and X-Ray data) and from different anatomical areas.
Artificial intelligence–enabled rapid diagnosis of patients with COVID-19
In this study, we used artificial intelligence (AI) algorithms to integrate chest CT findings with clinical symptoms, exposure history and laboratory testing to rapidly diagnose patients who are positive for COVID-19.
