Meta-Album PanNuke: 800 Microscopic Images of 19 Human Tissues

800 images of 19 different human tissue types, derived from the PanNuke dataset for nuclei segmentation. The images have been resized to 128x128 pixels and underwent stain normalization to reduce bias. This version was prepared by the Meta-Album project for classification tasks in March 2022.

Use Cases

• Training image classifiers to identify 19 different human tissue types based on microscopic images.
• Benchmarking model performance on a standardized, stain-normalized medical imaging dataset.
• Developing transfer learning techniques for small-scale medical image classification tasks.
• Studying the effects of stain normalization on model bias in histology data.

Strengths

• Contains 800 images across 20 classes, providing a focused benchmark for classification.
• Images are preprocessed to a uniform 128x128 pixel size and have stain normalization applied.
• Clear lineage from the well-cited PanNuke dataset, with original and Meta-Album citations provided.

Limitations

• Dataset size is relatively small with 800 images, which may limit model training without augmentation.
• The classification task is a transformation from an original segmentation dataset; the primary labels are tissue types, not nuclei.
• Description metadata is limited; actual data quality and class balance require manual inspection after download.

Provenance

Source

PanNuke dataset by Gamper et al., processed by the Meta-Album project.

Collection Method

Derived from a semi-automatically generated segmentation dataset and transformed into a classification task.

Freshness

Created 01 March 2022; last update date is unknown.