30 citations · 50 across the 9 of their papers we have counts for
3 papers · 1 filter
Beyond a Single Mode: GAN Ensembles for Diverse Medical Data Generation
Lorenzo Tronchin, Tommy Löfstedt, Paolo Soda +1
The advancement of generative AI, particularly in medical imaging, confronts the trilemma of ensuring high fidelity, diversity, and efficiency in synthetic data generation. While G…
Using Synthetic Images to Augment Small Medical Image Datasets
Minh H. Vu, Lorenzo Tronchin, Tufve Nyholm +1
Recent years have witnessed a growing academic and industrial interest in deep learning (DL) for medical imaging. To perform well, DL models require very large labeled datasets. Ho…
LatentAugment: Data Augmentation via Guided Manipulation of GAN's Latent Space
Lorenzo Tronchin, Minh H. Vu, Paolo Soda +1
Data Augmentation (DA) is a technique to increase the quantity and diversity of the training data, and by that alleviate overfitting and improve generalisation. However, standard D…