2019-05-24-qu19a.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	Weakly Supervised Deep Nuclei Segmentation using Points Annotation in Histopathology Images	Nuclei segmentation is a fundamental task in histopathological image analysis. Typically, such segmentation tasks require significant effort to manually generate pixel-wise annotations for fully supervised training. To alleviate the manual effort, in this paper we propose a novel approach using points only annotation. Two types of coarse labels with complementary information are derived from the points annotation, and are then utilized to train a deep neural network. The fully-connected conditional random field loss is utilized to further refine the model without introducing extra computational complexity during inference. Experimental results on two nuclei segmentation datasets reveal that the proposed method is able to achieve competitive performance compared to the fully supervised counterpart and the state-of-the-art methods while requiring significantly less annotation effort. Our code is publicly available.	inproceedings	Proceedings of Machine Learning Research	qu19a	0	Weakly Supervised Deep Nuclei Segmentation using Points Annotation in Histopathology Images	390	400	390-400	390	false	Qu, Hui and Wu, Pengxiang and Huang, Qiaoying and Yi, Jingru and Riedlinger, Gregory M. and De, Subhajyoti and Metaxas, Dimitris N.	given family Hui Qu given family Pengxiang Wu given family Qiaoying Huang given family Jingru Yi given family Gregory M. Riedlinger given family Subhajyoti De given family Dimitris N. Metaxas	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/qu19a/qu19a.pdf