Interpretable and Interactive Deep Multiple Instance Learning for Dental Caries Classification in Bitewing X-Rays
Abstract
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. Technically, our approach contributes in two ways: First, it outputs a heatmap of local patch classification probabilities despite being trained with weak image-level labels. Second, it is amenable to learning from segmentation labels to guide training. In contrast to existing methods, the human user can faithfully interpret predictions and interact with the model to decide which regions to attend to. Experiments are conducted on a large clinical dataset of 38k bitewings (316k teeth), where we achieve competitive performance compared to various baselines. When guided by an external caries segmentation model, a significant improvement in classification and localization performance is observed.
Cite
Text
Bergner et al. "Interpretable and Interactive Deep Multiple Instance Learning for Dental Caries Classification in Bitewing X-Rays." Medical Imaging with Deep Learning, 2023.Markdown
[Bergner et al. "Interpretable and Interactive Deep Multiple Instance Learning for Dental Caries Classification in Bitewing X-Rays." Medical Imaging with Deep Learning, 2023.](https://mlanthology.org/midl/2023/bergner2023midl-interpretable/)BibTeX
@inproceedings{bergner2023midl-interpretable,
title = {{Interpretable and Interactive Deep Multiple Instance Learning for Dental Caries Classification in Bitewing X-Rays}},
author = {Bergner, Benjamin and Rohrer, Csaba and Taleb, Aiham and Duchrau, Martha and De Leon, Guilherme and Rodrigues, Jonas and Schwendicke, Falk and Krois, Joachim and Lippert, Christoph},
booktitle = {Medical Imaging with Deep Learning},
year = {2023},
pages = {130-149},
volume = {172},
url = {https://mlanthology.org/midl/2023/bergner2023midl-interpretable/}
}