Noise-tolerant deep learning for histopathological image segmentation
Weizhi Li, Xiaoning Qian, Jim Ji · 2017
Inhomogeneous color distribution and intensity impose major difficulty in fully automated histopathological image (histo-image) segmentation. In this paper, we propose a novel deep learning framework for histo-image segmentation. We innovate a noise-tolerant layer to the output layer of a deep learning image segmentation framework U-Net, which alleviates the requirement of accurately segmented training images and enables “unsupervised” histo-image segmentation by taking noisy segmentation results of traditional image segmentation algorithms as the training outputs. We implement noise-tolerant U-Net for histo-image segmentation to study Duchenne Muscular Dystrophy (DMD), a muscle degenerative disease. Performance comparison with traditional algorithms and the original U-Net demonstrates the great potential of the proposed noise-tolerant U-Net for histo-image segmentation.