Noise-Tolerant Deep Learning for Histopathological Image Segmentation Conference Paper uri icon


  • © 2017 IEEE. 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.

author list (cited authors)

  • Li, W., Qian, X., & Ji, J.

citation count

  • 7

publication date

  • January 2017