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MHIST

MHIST is a binary classification task which comprises of 3,152 hematoxylin and eosin (H&E)-stained Formalin Fixed Paraffin-Embedded (FFPE) fixed-size images (224 by 224 pixels) of colorectal polyps from the Department of Pathology and Laboratory Medicine at Dartmouth-Hitchcock Medical Center (DHMC).

The tissue classes are: Hyperplastic Polyp (HP), Sessile Serrated Adenoma (SSA). This classification task focuses on the clinically-important binary distinction between HPs and SSAs, a challenging problem with considerable inter-pathologist variability. HPs are typically benign, while sessile serrated adenomas are precancerous lesions that can turn into cancer if left untreated and require sooner follow-up examinations. Histologically, HPs have a superficial serrated architecture and elongated crypts, whereas SSAs are characterized by broad-based crypts, often with complex structure and heavy serration.

Raw data

Key stats
Modality Vision (WSI patches)
Task Binary classification (2 classes)
Cancer type Colorectal Polyp
Data size 354 MB
Image dimension 224 x 224 x 3
Magnification (μm/px) 5x (2.0) *
Files format .png images
Number of images 3,152 (2,175 train, 977 test)
Splits in use annotations.csv (train / test)

* Downsampled from 40x to increase the field of view.

Organization

The contents from images.zip and the file annotations.csv from bmirds are organized as follows:

mhist                           # Root folder
├── images                      # All the dataset images
│   ├── MHIST_aaa.png
│   ├── MHIST_aab.png
│   ├── ...
└── annotations.csv             # The dataset annotations file

Download and preprocessing

To download the dataset, please visit the access portal on BMIRDS and follow the instructions. You will then receive an email with all the relative links that you can use to download the data (images.zip, annotations.csv, Dataset Research Use Agreement.pdf and MD5SUMs.txt).

Please create a root folder, e.g. mhist, and download all the files there, which unzipping the contents of images.zip to a directory named images inside your root folder (i.e. mhist/images). Afterwards, you can (optionally) delete the images.zip file.

Splits

We work with the splits provided by the data source. Since no "validation" split is provided, we use the "test" split as validation split.

  • Train split: annotations.csv :: "Partition" == "train"
  • Validation split: annotations.csv :: "Partition" == "test"
Splits Train Validation
#Samples 2,175 (69%) 977 (31%)