Search Results for author:
Found 5 papers, 3 papers with code
Interpretable and Interactive Deep Multiple Instance Learning for Dental Caries Classification in Bitewing X-rays
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.
ContIG: Self-supervised Multimodal Contrastive Learning for Medical Imaging with Genetics
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.
Self-Supervised Learning for 3D Medical Image Analysis using 3D SimCLR and Monte Carlo Dropout
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.
3D Self-Supervised Methods for Medical Imaging
Self-supervised learning methods have witnessed a recent surge of interest after proving successful in multiple application fields.
Multimodal Self-Supervised Learning for Medical Image Analysis
We introduce the multimodal puzzle task, which facilitates rich representation learning from multiple image modalities.
