most citedLess is More: Selective Reduction of CT Data for Self-Supervised Pre-Training of Deep Learning Models with Contrastive Learning Improves Downstream Classification Performance

5 citations · 9 across the 2 of their papers we have counts for

collaborators

2 papers

eess.IV2024★ 5 cited

Less is More: Selective Reduction of CT Data for Self-Supervised Pre-Training of Deep Learning Models with Contrastive Learning Improves Downstream Classification Performance

Daniel Wolf, Tristan Payer, Catharina Silvia Lisson +4

Self-supervised pre-training of deep learning models with contrastive learning is a widely used technique in image analysis. Current findings indicate a strong potential for contra…

eess.IV2024★ 4 cited

Input Data Adaptive Learning (IDAL) for Sub-acute Ischemic Stroke Lesion Segmentation

Michael Götz, Christian Weber, Christoph Kolb +1

In machine learning larger databases are usually associated with higher classification accuracy due to better generalization. This generalization may lead to non-optimal classifier…