1 code implementation • 17 Dec 2021 • Benjamin Bergner, Csaba Rohrer, Aiham Taleb, Martha Duchrau, Guilherme De Leon, Jonas Almeida Rodrigues, Falk Schwendicke, Joachim Krois, Christoph Lippert
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 radiographs.
1 code implementation • CVPR 2022 • Aiham Taleb, Matthias Kirchler, Remo Monti, Christoph Lippert
High annotation costs are a substantial bottleneck in applying modern deep learning architectures to clinically relevant medical use cases, substantiating the need for novel algorithms to learn from unlabeled data.
no code implementations • 29 Sep 2021 • Yamen Ali, Aiham Taleb, Marina M. -C. Höhne, Christoph Lippert
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 labeled data.
1 code implementation • NeurIPS 2020 • Aiham Taleb, Winfried Loetzsch, Noel Danz, Julius Severin, Thomas Gaertner, Benjamin Bergner, Christoph Lippert
Self-supervised learning methods have witnessed a recent surge of interest after proving successful in multiple application fields.
no code implementations • 11 Dec 2019 • Aiham Taleb, Christoph Lippert, Tassilo Klein, Moin Nabi
We introduce the multimodal puzzle task, which facilitates rich representation learning from multiple image modalities.