@inproceedings { reinke2022metrics,
	title = {Metrics Reloaded - A new recommendation framework for biomedical image analysis validation},
	journal = {Medical Imaging with Deep Learning},
	year = {2022},
	publisher = {MIDL 2022},
	abstract = {Meaningful performance assessment of biomedical image analysis algorithms depends on objective and appropriate performance metrics. There are major shortcomings in the current state of the art. Yet, so far limited attention has been paid to practical pitfalls associated when using particular metrics for image analysis tasks. Therefore, a number of international initiatives have collaborated to offer researchers with guidance and tools for selecting performance metrics in a problem-aware manner. In our proposed framework, the characteristics of the given biomedical problem are first captured in a problem fingerprint, which identifies properties related to domain interests, the target structure(s), the input datasets, and algorithm output. A problem category-specific mapping is applied in the second step to match fingerprints to metrics that reflect domain requirements. Based on input from experts from more than 60 institutions worldwide, we believe our metric recommendation framework to be useful to the MIDL community and to enhance the quality of biomedical image analysis algorithm validation.},
	url = {https://openreview.net/forum?id=24kBqy8rcB_},
	author = {Reinke, Annika and Christodoulou, Evangelia and Glocker, Ben and Scholz, Patrick and Isensee, Fabian and Kleesiek, Jens and Kozubek, Michal and Reyes, Mauricio and Riegler, Michael and Metrcis Matter {Consortium}}
}
