2019-05-24-bokhorst19a.md

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Contributed Papers	Learning from sparsely annotated data for semantic segmentation in histopathology images	We investigate the problem of building convolutional networks for semantic segmentation in histopathology images when weak supervision in the form of sparse manual annotations is provided in the training set. We propose to address this problem by modifying the loss function in order to balance the contribution of each pixel of the input data. We introduce and compare two approaches of loss balancing when sparse annotations are provided, namely (1) instance based balancing and (2) mini-batch based balancing. We also consider a scenario of full supervision in the form of dense annotations, and compare the performance of using either sparse or dense annotations with the proposed balancing schemes. Finally, we show that using a bulk of sparse annotations and a small fraction of dense annotations allows to achieve performance comparable to full supervision.	inproceedings	Proceedings of Machine Learning Research	bokhorst19a	0	Learning from sparsely annotated data for semantic segmentation in histopathology images	84	91	84-91	84	false	Bokhorst, {John-Melle} and Pinckaers, Hans and {van Zwam}, Peter and Nagtegaal, Iris and {van der Laak}, Jeroen and Ciompi, Francesco	given family John-Melle Bokhorst given family Hans Pinckaers given prefix family Peter van Zwam given family Iris Nagtegaal given prefix family Jeroen van der Laak given family Francesco Ciompi	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/bokhorst19a/bokhorst19a.pdf