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Found 3 papers, 2 papers with code
Overcoming Data Scarcity in Biomedical Imaging with a Foundational Multi-Task Model
The UMedPT foundational model outperformed ImageNet pretraining and the previous state-of-the-art models.
Image prediction of disease progression by style-based manifold extrapolation
We present a generic solution for this problem by a methodology that allows the prediction of progression risk and morphology in individuals using a latent extrapolation optimization approach.
Advancing diagnostic performance and clinical usability of neural networks via adversarial training and dual batch normalization
Contrary to previous research on adversarially trained models, we found that the accuracy of such models was equal to standard models when sufficiently large datasets and dual batch norm training were used.
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