pneumoniamnist (MedMNIST)

Source: pneumoniamnist
Task: binary-class
Resolutions: 224x224
License: CC BY 4.0

Description

The PneumoniaMNIST is based on a prior dataset of 5,856 pediatric chest X-Ray images. The task is binary-class classification of pneumonia against normal. We split the source training set with a ratio of 9:1 into training and validation set and use its source validation set as the test set. The source images are gray-scale, and their sizes are (384−2,916)×(127−2,713). We center-crop the images and resize them into 1×28×28.

Config naming convention

{split}-{class}-{res}

split : train | val | test
class : all   | <sanitized class name>
res   : res28 | res64 | res128 | res224

Loading examples

from datasets import load_dataset

# All training images at 224px
ds = load_dataset('.../pneumoniamnist', 'train-all-res224', split='train')

# Only 'normal' class, training split
ds = load_dataset('.../pneumoniamnist', 'train-normal-res224', split='train')

Class labels

• 0 — normal (config key: normal)
• 1 — pneumonia (config key: pneumonia)

Class distribution

224x224

train (N=4,708, IR=2.88x)

Class	Config key	Count	Share
normal	normal	1,214	25.8%
pneumonia	pneumonia	3,494	74.2%

val (N=524, IR=2.88x)

Class	Config key	Count	Share
normal	normal	135	25.8%
pneumonia	pneumonia	389	74.2%

test (N=624, IR=1.67x)

Class	Config key	Count	Share
normal	normal	234	37.5%
pneumonia	pneumonia	390	62.5%

Citation

@article{medmnistv2,
  title={MedMNIST v2 - A large-scale lightweight benchmark for 2D and 3D biomedical image classification},
  author={Yang, Jiancheng and Shi, Rui and Wei, Donglai and Liu, Zequan
          and Zhao, Lin and Ke, Bilian and Pfister, Hanspeter and Ni, Bingbing},
  journal={Scientific Data},
  year={2023}
}