2024-01-23-sun24a.md

title	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Inherently Interpretable Multi-Label Classification Using Class-Specific Counterfactuals	Interpretability is essential for machine learning algorithms in high-stakes application fields such as medical image analysis. However, high-performing black-box neural networks do not provide explanations for their predictions, which can lead to mistrust and suboptimal human-ML collaboration. Post-hoc explanation techniques, which are widely used in practice, have been shown to suffer from severe conceptual problems. Furthermore, as we show in this paper, current explanation techniques do not perform adequately in the multi-label scenario, in which multiple medical findings may co-occur in a single image. We propose Attri-Net, an inherently interpretable model for multi-label classification. Attri-Net is a powerful classifier that provides transparent, trustworthy, and human-understandable explanations. The model first generates class-specific attribution maps based on counterfactuals to identify which image regions correspond to certain medical findings. Then a simple logistic regression classifier is used to make predictions based solely on these attribution maps. We compare Attri-Net to five post-hoc explanation techniques and one inherently interpretable classifier on three chest X-ray datasets. We find that Attri-Net produces high-quality multi-label explanations consistent with clinical knowledge and has comparable classification performance to state-of-the-art classification models.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	sun24a	0	Inherently Interpretable Multi-Label Classification Using Class-Specific Counterfactuals	937	956	937-956	937	false	Sun, Susu and Woerner, Stefano and Maier, Andreas and Koch, Lisa M. and Baumgartner, Christian F.	given family Susu Sun given family Stefano Woerner given family Andreas Maier given family Lisa M. Koch given family Christian F. Baumgartner	2024-01-23		Medical Imaging with Deep Learning	227	inproceedings	date-parts 2024 1 23	https://proceedings.mlr.press/v227/sun24a/sun24a.pdf