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Cluster Entropy: active learning sampling methods for WSI. 2022卒-備瀬研-荒木健吾

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AL_SSDA_WSI

The codes are used for the experiments of MICCAI2022
2022卒-備瀬研-荒木健吾

Directory

  • analysis:
    To analyze about the dataset.
  • clustering:
    Extract feature vectors from source trained model, and calculate cluster entropy.
  • preprocess:
    To crop WSI into patches and split WSIs into train/valid/test.
  • S:
    The codes for the experiment S that use only labeled source.
  • ST:
    The codes for the experiment S+T that use labeled source and labeled target.
  • visualyze:
    some functions for visualyzing featuremap and results of cluster entropy.

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Cluster Entropy: active learning sampling methods for WSI. 2022卒-備瀬研-荒木健吾

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