datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
PatchCamelyon
PatchCamelyon (PCam)
Description
The PatchCamelyon benchmark is a new and challenging image classification dataset. It consists of 327.680 color images (96 x 96px) extracted from histopathologic scans of lymph node sections. Each image is annoted with a binary label indicating presence of metastatic tissue. PCam provides a new benchmark for machine learning models: bigger than CIFAR10, smaller than imagenet, trainable on a single GPU
Why PCam
Fundamental… See the full description on the dataset page: https://huggingface.co/datasets/1aurent/PatchCamelyon.PatchCamelyon
PatchCamelyon (PCam)
This is a reupload of the PatchCamelyon (PCam) dataset to make it more readily usable instead of manipulating H5 files. The original can be found in the author's Github repo.
If you use this dataset, please cite the original publications:
@inproceedings{veeling2018rotation,
title={Rotation Equivariant CNNs for Digital Pathology},
author={Veeling, Bastiaan S and Linmans, Jasper and Winkens, Jim and Cohen, Taco and Welling, Max},
booktitle={Medical Image… See the full description on the dataset page: https://huggingface.co/datasets/zacharielegault/PatchCamelyon.PatchCamelyon
PatchCamelyon (PCam)
Description
The PatchCamelyon benchmark is a new and challenging image classification dataset. It consists of 327.680 color images (96 x 96px) extracted from histopathologic scans of lymph node sections. Each image is annoted with a binary label indicating presence of metastatic tissue. PCam provides a new benchmark for machine learning models: bigger than CIFAR10, smaller than imagenet, trainable on a single GPU
Why PCam
Fundamental… See the full description on the dataset page: https://huggingface.co/datasets/pavan316/PatchCamelyon.PatchCamelyon
PatchCamelyon (PCam)
Description
The PatchCamelyon benchmark is a new and challenging image classification dataset. It consists of 327.680 color images (96 x 96px) extracted from histopathologic scans of lymph node sections. Each image is annoted with a binary label indicating presence of metastatic tissue. PCam provides a new benchmark for machine learning models: bigger than CIFAR10, smaller than imagenet, trainable on a single GPU
Why PCam
Fundamental… See the full description on the dataset page: https://huggingface.co/datasets/KE9037/PatchCamelyon.