most citedSelf-Supervised Pre-Training with Contrastive and Masked Autoencoder Methods for Dealing with Small Datasets in Deep Learning for Medical Imaging

55 citations · 66 across the 3 of their papers we have counts for

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cs.CV2024★ 6 cited

Evaluating the Explainability of Attributes and Prototypes for a Medical Classification Model

Luisa Gallée, Catharina Silvia Lisson, Christoph Gerhard Lisson +5

Due to the sensitive nature of medicine, it is particularly important and highly demanded that AI methods are explainable. This need has been recognised and there is great research…

cs.CV2023★ 55 cited

Self-Supervised Pre-Training with Contrastive and Masked Autoencoder Methods for Dealing with Small Datasets in Deep Learning for Medical Imaging

Daniel Wolf, Tristan Payer, Catharina Silvia Lisson +4

Deep learning in medical imaging has the potential to minimize the risk of diagnostic errors, reduce radiologist workload, and accelerate diagnosis. Training such deep learning mod…