2024-12-23-dauchelle24a.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
An unexpected confounder: how brain shape can be used to classify MRI scans ?	Although deep learning has proved its effectiveness in the analysis of medical images, its great ability to extract complex features makes it susceptible to base its decision on spurious confounders present in the images. However, especially for medical applications, network decisions must be based on relevant elements. Numerous confounding factors have been identified in the case of brain scans such as gender, age, MRI sites or scanners, etc. Nevertheless, although skull stripping is a classic preprocessing step for brain scans, brain shape has never been considered as a possible confounder. In this work, we show that brain shape is used in the classification of brain MRI scans from different databases, even when it should not be considered as a clinically relevant factor. To this purpose, we introduce a rigorous two steps method to assess whether a factor is a confounder or not, and we apply it to identify the brain shape as a confounding variable in brain images classification. Lastly, we propose to use a deformable registration in the data preprocessing pipeline to align the brain contours of the images in the datasets, whereas standard pipelines often do nothing more than affine registration. Including this deformable registration step makes the classification free from the brain shape confounding effect.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	dauchelle24a	0	An unexpected confounder: how brain shape can be used to classify MRI scans ?	338	351	338-351	338	false	Dauchelle, Valentine Wargnier and Grenier, Thomas and Sdika, Micha\"el	given family Valentine Wargnier Dauchelle given family Thomas Grenier given family Michaël Sdika	2024-12-23		Proceedings of The 7nd International Conference on Medical Imaging with Deep Learning	250	inproceedings	date-parts 2024 12 23	https://raw.githubusercontent.com/mlresearch/v250/main/assets/dauchelle24a/dauchelle24a.pdf