2019-05-24-chen19a.md

section	title	abstract	layout	series	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	publisher	container-title	volume	genre	issued	pdf	extras
Contributed Papers	Deep Hierarchical Multi-label Classification of Chest X-ray Images	Chest X-rays (CXRs) are a crucial and extraordinarily common diagnostic tool, leading to heavy research for Computer-Aided Diagnosis (CAD) solutions. However, both high classification accuracy and meaningful model predictions that respect and incorporate clinical taxonomies are crucial for CAD usability. To this end, we present a deep Hierarchical Multi-Label Classification (HMLC) approach for CXR CAD. Different than other hierarchical systems, we show that first training the network to model conditional probability directly and then refining it with unconditional probabilities is key in boosting performance. In addition, we also formulate a numerically stable cross-entropy loss function for unconditional probabilities that provides concrete performance improvements. To the best of our knowledge, we are the first to apply HMLC to medical imaging CAD. We extensively evaluate our approach on detecting 14 abnormality labels from the PLCO dataset, which comprises 198,000 manually annotated CXRs. We report a mean Area Under the Curve (AUC) of 0.887, the highest yet reported for this dataset. These performance improvements, combined with the inherent usefulness of taxonomic predictions, indicate that our approach represents a useful step forward for CXR CAD.	inproceedings	Proceedings of Machine Learning Research	chen19a	0	Deep Hierarchical Multi-label Classification of Chest X-ray Images	109	120	109-120	109	false	Chen, Haomin and Miao, Shun and Xu, Daguang and Hager, Gregory D. and Harrison, Adam P.	given family Haomin Chen given family Shun Miao given family Daguang Xu given family Gregory D. Hager given family Adam P. Harrison	2019-05-24		PMLR	Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning	102	inproceedings	date-parts 2019 5 24	http://proceedings.mlr.press/v102/chen19a/chen19a.pdf