2019-05-24-roulet19a.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	Joint Learning of Brain Lesion and Anatomy Segmentation from Heterogeneous Datasets	Brain lesion and anatomy segmentation in magnetic resonance images are fundamental tasks in neuroimaging research and clinical practice. Given enough training data, convolutional neuronal networks (CNN) proved to outperform all existent techniques in both tasks independently. However, to date, little work has been done regarding simultaneous learning of brain lesion and anatomy segmentation from disjoint datasets. In this work we focus on training a single CNN model to predict brain tissue and lesion segmentations using heterogeneous datasets labeled independently, according to only one of these tasks (a common scenario when using publicly available datasets). We show that label contradiction issues can arise in this case, and propose a novel \textit{adaptive cross entropy} (ACE) loss function that makes such training possible. We provide quantitative evaluation in two different scenarios, benchmarking the proposed method in comparison with a multi-network approach. Our experiments suggest ACE loss enables training of single models when standard cross entropy and Dice loss functions tend to fail. Moreover, we show that it is possible to achieve competitive results when comparing with multiple networks trained for independent tasks.	inproceedings	Proceedings of Machine Learning Research	roulet19a	0	Joint Learning of Brain Lesion and Anatomy Segmentation from Heterogeneous Datasets	401	413	401-413	401	false	Roulet, Nicolas and Slezak, Diego Fernandez and Ferrante, Enzo	given family Nicolas Roulet given family Diego Fernandez Slezak given family Enzo Ferrante	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/roulet19a/roulet19a.pdf