TIGER: Training Data for Tumor-Infiltrating Lymphocyte Assessment in Breast Cancer

Training data for the first challenge on fully automated assessment of tumor-infiltrating lymphocytes (TILs) in breast cancer histopathology slides. The dataset was contributed by the Radboud University Medical Center and is hosted on AWS Open Data. TILs are an important biomarker that can help target immunotherapy and reduce the need for more aggressive treatments.

Use Cases

• Training deep learning models for automated TIL detection based on histopathology slide images.
• Benchmarking computer vision algorithms for quantifying immune cell infiltration in breast cancer tissue.
• Developing predictive models for immunotherapy response based on TIL assessment mentioned in the description.

Strengths

• Data is associated with the first challenge for automated TIL assessment, providing a benchmark purpose.
• Dataset is hosted on a reliable, scalable platform (AWS Open Data).

Limitations

• Row count and dataset size are unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Last update date is unknown; freshness unverified.

Provenance

Source

Radboud University Medical Center

Collection Method

Likely contains annotated histopathology slide images for a medical AI challenge.

Time Range

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Freshness

Last updated: unknown

Geography

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