histopathology / README.md
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metadata
license: mit
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
dataset_info:
  features:
    - name: image
      dtype: image
    - name: slide_name
      dtype: string
    - name: x
      dtype: int64
    - name: 'y'
      dtype: int64
    - name: level
      dtype: int64
    - name: patch_size
      sequence: int64
    - name: resize
      sequence: int64
    - name: embedding_vector
      sequence:
        sequence: float32
  splits:
    - name: train
      num_bytes: 7855046412.21
      num_examples: 85283
  download_size: 7915527673
  dataset_size: 7855046412.21

Dataset Card for Histopathology Dataset

Dataset Summary

This dataset contains 224x224, 512x512 and 1024x1024 patches of a group of histopathology images taken from the CAMELYON16 dataset and embedding vectors extracted from these patches using the Google Path Foundation model.

Data Processing

Thumbnail of Main Slide

Main Slide Thumbnail

Usage

from datasets import load_dataset

dataset = load_dataset("Cilem/histopathology")
display(dataset['train'][0]["image"])

Supported Tasks

Machine learning applications that can be performed using this dataset:

  • Classification
  • Segmentation
  • Image generation

Languages

  • English

Dataset Structure

Data Fields

  • image: Image of the patch.
  • slide_name: Main slide name of the patch.
  • x: X coordinate of the patch.
  • y: Y coordinate of the patch.
  • level: Level of the main slide.
  • patch_size: Size of the patch.
  • resize: Image size used to obtain embedding vector with Path foundation model.
  • embedding_vector: Embedding vector of the patch extracted using Path foundation model.

Dataset Creation

Source Data

  • Original Sources
    • CAMELYON16: List of images taken from CAMELYON16 dataset:
      • test_001.tif
      • test_002.tif
      • test_003.tif
      • test_004.tif
      • test_005.tif
      • test_006.tif
      • test_007.tif
      • test_008.tif
      • test_009.tif
    • Google Path Foundation: Embedding vectors extracted from the patches using the Path Foundation model.

Considerations for Using the Data

Social Impact and Bias

Attention should be paid to the Path Foundation model licenses provided by Google.