2019-05-24-zhang19a.md

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Contributed Papers	SPDA: Superpixel-based Data Augmentation for Biomedical Image Segmentation	Supervised training a deep neural network aims to “teach” the network to mimic human visual perception that is represented by image-and-label pairs in the training data. Superpixelized (SP) images are visually perceivable to humans, but a conventionally trained deep learning model often performs poorly when working on SP images. To better mimic human visual perception, we think it is desirable for the deep learning model to be able to perceive not only raw images but also SP images. In this paper, we propose a new superpixel-based data augmentation (SPDA) method for training deep learning models for biomedical image segmentation. Our method applies a superpixel generation scheme to all the original training images to generate superpixelized images. The SP images thus obtained are then jointly used with the original training images to train a deep learning model. Our experiments of SPDA on four biomedical image datasets show that SPDA is effective and can consistently improve the performance of state-of-the-art fully convolutional networks for biomedical image segmentation in 2D and 3D images. Additional studies also demonstrate that SPDA can practically reduce the generalization gap.	inproceedings	Proceedings of Machine Learning Research	zhang19a	0	SPDA: Superpixel-based Data Augmentation for Biomedical Image Segmentation	572	587	572-587	572	false	Zhang, Yizhe and Yang, Lin and Zheng, Hao and Liang, Peixian and Mangold, Colleen and Loreto, Raquel G. and Hughes, David P. and Chen, Danny Z.	given family Yizhe Zhang given family Lin Yang given family Hao Zheng given family Peixian Liang given family Colleen Mangold given family Raquel G. Loreto given family David P. Hughes given family Danny Z. Chen	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/zhang19a/zhang19a.pdf