@inproceedings{1c642b4f0b6846bd84988611ff45c82e,
title = "Anatomy-Driven Pathology Detection on Chest X-rays",
abstract = "Pathology detection and delineation enables the automatic interpretation of medical scans such as chest X-rays while providing a high level of explainability to support radiologists in making informed decisions. However, annotating pathology bounding boxes is a time-consuming task such that large public datasets for this purpose are scarce. Current approaches thus use weakly supervised object detection to learn the (rough) localization of pathologies from image-level annotations, which is however limited in performance due to the lack of bounding box supervision. We therefore propose anatomy-driven pathology detection (ADPD), which uses easy-to-annotate bounding boxes of anatomical regions as proxies for pathologies. We study two training approaches: supervised training using anatomy-level pathology labels and multiple instance learning (MIL) with image-level pathology labels. Our results show that our anatomy-level training approach outperforms weakly supervised methods and fully supervised detection with limited training samples, and our MIL approach is competitive with both baseline approaches, therefore demonstrating the potential of our approach.",
keywords = "Anatomical regions, Chest X-rays, Pathology detection",
author = "Philip M{\"u}ller and Felix Meissen and Johannes Brandt and Georgios Kaissis and Daniel Rueckert",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023 ; Conference date: 08-10-2023 Through 12-10-2023",
year = "2023",
doi = "10.1007/978-3-031-43907-0_6",
language = "English",
isbn = "9783031439063",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "57--66",
editor = "Hayit Greenspan and Hayit Greenspan and Anant Madabhushi and Parvin Mousavi and Septimiu Salcudean and James Duncan and Tanveer Syeda-Mahmood and Russell Taylor",
booktitle = "Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 - 26th International Conference, Proceedings",
}