128 citations · 137 across the 4 of their papers we have counts for
5 papers
Interpretable and Interactive Deep Multiple Instance Learning for Dental Caries Classification in Bitewing X-rays
Benjamin Bergner, Csaba Rohrer, Aiham Taleb +6
We propose a simple and efficient image classification architecture based on deep multiple instance learning, and apply it to the challenging task of caries detection in dental rad…
ContIG: Self-supervised Multimodal Contrastive Learning for Medical Imaging with Genetics
Aiham Taleb, Matthias Kirchler, Remo Monti +1
High annotation costs are a substantial bottleneck in applying modern deep learning architectures to clinically relevant medical use cases, substantiating the need for novel algori…
Self-Supervised Learning for 3D Medical Image Analysis using 3D SimCLR and Monte Carlo Dropout
Yamen Ali, Aiham Taleb, Marina M. -C. Höhne +1
Self-supervised learning methods can be used to learn meaningful representations from unlabeled data that can be transferred to supervised downstream tasks to reduce the need for l…
3D Self-Supervised Methods for Medical Imaging
Aiham Taleb, Winfried Loetzsch, Noel Danz +4
Self-supervised learning methods have witnessed a recent surge of interest after proving successful in multiple application fields. In this work, we leverage these techniques, and…
Multimodal Self-Supervised Learning for Medical Image Analysis
Aiham Taleb, Christoph Lippert, Tassilo Klein +1
Self-supervised learning approaches leverage unlabeled samples to acquire generic knowledge about different concepts, hence allowing for annotation-efficient downstream task learni…