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Camelyon17 - Breast Cancer Metastasis Detection in Pathology

Pathology slide annotation for breast cancer metastasis detection. Based on the CAMELYON grand challenge (Ehteshami Bejnordi et al., JAMA 2017), annotators delineate tumor regions in whole-slide histopathology images and classify slides as positive or negative for metastasis.

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Configuration Fileconfig.yaml

This Potato config reproduces the annotation task. Save it as config.yaml and run potato start config.yaml to try it.

yaml
# Camelyon17 - Breast Cancer Metastasis Detection in Pathology
# Based on Ehteshami Bejnordi et al., JAMA 2017
# Paper: https://jamanetwork.com/journals/jama/fullarticle/2665774
# Dataset: https://camelyon17.grand-challenge.org/
#
# Pathology whole-slide image annotation for detecting breast cancer metastases
# in sentinel lymph node tissue. Annotators outline tumor regions using polygon
# and bounding box tools, then classify the overall slide-level diagnosis.
#
# Region Labels:
# - Tumor Region: Areas containing metastatic cancer cells
# - Normal Tissue: Healthy lymph node tissue
# - Artifact: Tissue folding, bubbles, or staining artifacts
# - Background: Non-tissue areas (glass, mounting medium)
#
# Annotation Guidelines:
# 1. Examine the histopathology image at available magnification
# 2. Use polygon tool for precise tumor boundary delineation
# 3. Use bounding box for quick region-of-interest marking
# 4. Classify the slide as positive, negative, or uncertain for metastasis
# 5. Tumor cells appear as dense, darkly stained clusters with irregular nuclei

annotation_task_name: "Camelyon17 - Pathology Metastasis Detection"
task_dir: "."

data_files:
  - sample-data.json

item_properties:
  id_key: "id"
  text_key: "text"

output_annotation_dir: "annotation_output/"
output_annotation_format: "json"

port: 8000
server_name: localhost

annotation_schemes:
  # Step 1: Annotate regions in the pathology image
  - annotation_type: image_annotation
    name: tissue_regions
    description: "Delineate tissue regions in the pathology slide. Use polygon for precise boundaries, bounding box for quick marking."
    tools:
      - polygon
      - bbox
    labels:
      - "Tumor Region"
      - "Normal Tissue"
      - "Artifact"
      - "Background"

  # Step 2: Slide-level classification
  - annotation_type: radio
    name: slide_diagnosis
    description: "What is the overall slide-level diagnosis for metastasis?"
    labels:
      - "Positive (Metastasis Present)"
      - "Negative (No Metastasis)"
      - "Uncertain"
    keyboard_shortcuts:
      "Positive (Metastasis Present)": "1"
      "Negative (No Metastasis)": "2"
      "Uncertain": "3"
    tooltips:
      "Positive (Metastasis Present)": "One or more tumor regions are clearly identifiable in the slide"
      "Negative (No Metastasis)": "No tumor cells are visible; the tissue appears entirely normal"
      "Uncertain": "Suspicious regions are present but a definitive diagnosis cannot be made"

annotation_instructions: |
  You will annotate histopathology images of sentinel lymph node tissue sections
  for the presence of breast cancer metastases.

  For each slide:
  1. Examine the tissue section carefully at available magnification.
  2. Use the polygon tool to precisely outline any tumor regions you identify.
     - Tumor cells typically appear as dense clusters with large, irregular,
       darkly stained nuclei.
     - Metastases may appear as isolated tumor cells (ITC), micrometastases,
       or macrometastases.
  3. Use the bounding box tool for quick marking of suspicious areas.
  4. Label non-tumor regions as Normal Tissue, Artifact, or Background.
  5. Provide an overall slide-level diagnosis.

  Important:
  - Artifacts (tissue folds, air bubbles, poor staining) should not be confused with tumor.
  - When uncertain, mark as Uncertain and outline the suspicious region.
  - This task requires familiarity with histopathology.

html_layout: |
  <div style="padding: 15px; max-width: 900px; margin: auto;">
    <div style="display: flex; gap: 12px; margin-bottom: 14px; flex-wrap: wrap;">
      <div style="background: #fce4ec; padding: 7px 14px; border-radius: 8px;">
        <strong>Tissue Type:</strong> {{tissue_type}}
      </div>
    </div>
    <div style="text-align: center; margin-bottom: 16px; background: #212121; padding: 12px; border-radius: 8px;">
      <img src="{{image_url}}" style="max-width: 100%; max-height: 600px; border-radius: 4px;" alt="Histopathology slide" />
    </div>
    <div style="background: #f0f9ff; border: 1px solid #bae6fd; border-radius: 8px; padding: 16px; margin-bottom: 16px;">
      <strong style="color: #0369a1;">Slide Description:</strong>
      <p style="font-size: 15px; line-height: 1.7; margin: 8px 0 0 0;">{{text}}</p>
    </div>
  </div>

allow_all_users: true
instances_per_annotator: 30
annotation_per_instance: 3
allow_skip: true
skip_reason_required: false

Sample Datasample-data.json

json
[
  {
    "id": "camelyon_001",
    "text": "Sentinel lymph node section from left axilla, H&E stained, 20x magnification. Dense cellular region visible in the subcapsular sinus area with irregular nuclear morphology.",
    "image_url": "https://example.com/camelyon/slide_001.png",
    "tissue_type": "Sentinel Lymph Node"
  },
  {
    "id": "camelyon_002",
    "text": "Lymph node tissue section showing predominantly normal germinal centers with reactive follicular hyperplasia. No obvious atypical cells at scanning magnification.",
    "image_url": "https://example.com/camelyon/slide_002.png",
    "tissue_type": "Sentinel Lymph Node"
  }
]

// ... and 8 more items

Get This Design

View on GitHub

Clone or download from the repository

Quick start:

git clone https://github.com/davidjurgens/potato-showcase.git
cd potato-showcase/image/medical/camelyon-pathology
potato start config.yaml

Dataset & paper

Ehteshami Bejnordi et al., JAMA 2017

Citation (BibTeX)

bibtex
@article{bejnordi2017camelyon,
    title = "Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer",
    author = "Ehteshami Bejnordi, Babak and Veta, Mitko and van Diest, Paul Johannes and van Ginneken, Bram and Karssemeijer, Nico and Litjens, Geert and van der Laak, Jeroen A.W.M. and the CAMELYON16 Consortium",
    journal = "JAMA",
    volume = "318",
    number = "22",
    pages = "2199--2210",
    year = "2017",
    doi = "10.1001/jama.2017.14585",
    url = "https://jamanetwork.com/journals/jama/fullarticle/2665774"
}

Details

Annotation Types

image_annotationradio

Domain

Medical ImagingPathology

Use Cases

Cancer DetectionHistopathology AnalysisMedical Diagnosis

Tags

camelyonpathologycancermetastasiswhole-slidehistologyjama2017medical

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