PathMNIST

Dataset Information

The PathMNIST is based on a prior study for predicting survival from colorectal cancer histology slides, providing a dataset (NCT-CRC-HE-100K) of 100,000 non-overlapping image patches from hematoxylin & eosin stained histological images, and a test dataset (CRC-VAL-HE-7K) of 7,180 image patches from a different clinical center. The dataset is comprised of 9 types of tissues, resulting in a multi-class classification task. We resize the source images of 3×224×224 into 3×28×28, and split NCT-CRC-HE-100K into training and validation set with a ratio of 9:1. The CRC-VAL-HE-7K is treated as the test set.

Task: multi-class

Labels:

0: adipose, 1: background, 2: debris, 3: lymphocytes, 4: mucus, 5: smooth muscle, 6: normal colon mucosa, 7: cancer-associated stroma, 8: colorectal adenocarcinoma epithelium

Samples:

• Train: 89996
• Validation: 10004
• Test: 7180

Experiment Parameter

Learning Rate: 3e-4

Training Epoch: 50

Convergence Epoch: 7