Iterative learning to make the most of unlabeled and quickly obtained labeled data in histologyDownload PDF

10 Dec 2018 (modified: 24 Jun 2019)MIDL 2019 Conference Full SubmissionReaders: Everyone
  • Keywords: Digital pathology, convolutional neural networks, kidney, segmentation, weakly supervised.
  • TL;DR: Weakly-, and un- supervised segmentation method for histological image data; training with minimum and imprecisely labeled data
  • Abstract: Due to the increasing availability of digital whole slide scanners, the importance of image analysis in the field of digital pathology increased significantly. A major challenge and an equally big opportunity for analyses in this field is given by the wide range of tasks and different histological stains. Although sufficient image data is often available for training, the requirement for corresponding expert annotations inhibits clinical deployment. Thus, there is an urgent need for methods which can be effectively trained with or adapted to a small amount of labeled training data. Here, we propose a method for optimizing the overall trade-off between (low) annotation effort and (high) segmentation accuracy. For this purpose, we propose an approach based on a weakly supervised and an unsupervised learning stage relying on few roughly labeled samples and many unlabeled samples. Although the idea of weakly annotated data is not new, we firstly investigate the applicability to digital pathology in a state-of-the-art machine learning setting.
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