GlaS Dataset

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Visualization of the GlaS Train Dataset in the Deep Lake UI

GlaS dataset

What is GlaS Dataset?

The GlaS (Gland Segmentation) Dataset is created to encourage research in gland segmentation algorithms on images of hematoxylin and Eosin (H&E) stained slides (consists of a variety of histologic grades). The dataset ground truth annotations have been annotated by expert pathologists. There are 37 benign and 48 malignant training images. The testing dataset contains 37 benign and 43 malignant images. In total, the dataset contains 165 images.

Download GlaS Dataset in Python

Instead of downloading the GlaS dataset in Python, you can effortlessly load it in Python via our Deep Lake open source with just one line of code.

Load GlaS Dataset Training Subset in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/glas-train")

Load GlaS Dataset Testing Subset in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/glas-test")

GlaS Dataset Structure

GlaS Data Fields

images: tensor containing images of the hematoxylin and Eosin (H&E) stained slides.
masks: tensor containing a segmented area of their respective images.
grade_Glas: tensor containing labels of cancer type.
grade_Sirinukunwattana: tensor containing labels of patient health patient_ids: tensor containing patient ids.

How to use GlaS Dataset with PyTorch and TensorFlow in Python

Train a model on the GlaS dataset with PyTorch in Python

Let’s use Deep Lake built-in PyTorch one-line dataloader to connect the data to the compute:

				
					dataloader = ds.pytorch(num_workers=0, batch_size=4, shuffle=False)

Train a model on the GlaS dataset with TensorFlow in Python

				
					dataloader = ds.tensorflow()

Additional Information about GlaS Dataset

GlaS Dataset Description

Homepage: https://warwick.ac.uk/fac/cross_fac/tia/data/glascontest/
Paper: Sirinukunwattana, K., Snead, D. R., & Rajpoot, N. M. (2015). A stochastic polygons model for glandular structures in colon histology images. IEEE transactions on medical imaging, 34(11), 2366-2378.

GlaS Dataset Curators

Sirinukunwattana, Korsuk, David RJ Snead, and Nasir M. Rajpoot.

GlaS Dataset Licensing Information

Deep Lake users may have access to a variety of publicly available datasets. We do not host or distribute these datasets, vouch for their quality or fairness, or claim that you have a license to use the datasets. It is your responsibility to determine whether you have permission to use the datasets under their license.

If you’re a dataset owner and do not want your dataset to be included in this library, please get in touch through a GitHub issue. Thank you for your contribution to the ML community!

GlaS Dataset Citation Information

				
					@article{sirinukunwattana2015stochastic,
  title={A stochastic polygons model for glandular structures in colon histology images},
  author={Sirinukunwattana, Korsuk and Snead, David RJ and Rajpoot, Nasir M},
  journal={IEEE transactions on medical imaging},
  volume={34},
  number={11},
  pages={2366--2378},
  year={2015},
  publisher={IEEE}
}