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Learning Visual Features by Colorization for Slide-Consistent Survival Prediction from Whole Slide Images

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Part of the Lecture Notes in Computer Science book series (LNIP,volume 12908)

Abstract

Recent deep learning techniques have shown promising performance on survival prediction from Whole Slide Images (WSIs). These methods are often based on multiple-step frameworks including patch sampling, feature extraction, and feature aggregation. However, feature extraction typically relies on handcrafted features or Convolutional Neural Networks (CNNs) pretrained on ImageNet without fine-tuning, thus leading to suboptimal performance. Besides, to aggregate features, previous studies focus on WSI-level survival prediction but ignore the heterogeneous information that is present in multiple WSIs acquired for the same patient. To address the above challenges, we propose a survival prediction model that exploits heterogeneous features at the patient-level. Specifically, we introduce colorization as the pretext task to train the CNNs which are tailored for extracting features from patches of WSIs. In addition, we develop a patient-level framework integrating multiple WSIs for survival prediction with consistency and ranking losses. Extensive experiments show that our model achieves state-of-the-art performance on two large-scale public datasets.

Keywords

  • Survival prediction
  • Histopathology
  • Colorization
  • Whole Slide Images
  • Deep learning

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Notes

  1. 1.

    https://portal.gdc.cancer.gov/.

  2. 2.

    We gratefully acknowledge the help and suggestions from the authors of RankSurv.

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Fan, L., Sowmya, A., Meijering, E., Song, Y. (2021). Learning Visual Features by Colorization for Slide-Consistent Survival Prediction from Whole Slide Images. In: , et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12908. Springer, Cham. https://doi.org/10.1007/978-3-030-87237-3_57

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  • DOI: https://doi.org/10.1007/978-3-030-87237-3_57

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