2019-05-24-gupta19a.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	Iterative learning to make the most of unlabeled and quickly obtained labeled data in histology	Due to the increasing availability of digital whole slide scanners, the importance of image analysis in the field of digital pathology increased significantly. A major challenge and an equally big opportunity for analyses in this field is given by the wide range of tasks and different histological stains. Although sufficient image data is often available for training, the requirement for corresponding expert annotations inhibits clinical deployment. Thus, there is an urgent need for methods which can be effectively trained with or adapted to a small amount of labeled training data. Here, we propose a method to find an optimum trade-off between (low) annotation effort and (high) segmentation accuracy. For this purpose, we propose an approach based on a weakly supervised and an unsupervised learning stage relying on few roughly labeled samples and many unlabeled samples. Although the idea of weakly annotated data is not new, we firstly investigate the applicability to digital pathology in a state-of-the-art machine learning setting.	inproceedings	Proceedings of Machine Learning Research	gupta19a	0	Iterative learning to make the most of unlabeled and quickly obtained labeled data in histology	215	224	215-224	215	false	Gupta, Laxmi and {Mara Klinkhammer}, Barbara and Boor, Peter and Merhof, Dorit and Gadermayr, Michael	given family Laxmi Gupta given family Barbara Mara Klinkhammer given family Peter Boor given family Dorit Merhof given family Michael Gadermayr	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/gupta19a/gupta19a.pdf