i3S Annotated Datasets on Digital Pathology

WELCOME

In an effort to contribute and push forward the field of Digital Pathology, Ipatimup and INEB have joined forces in the construction of histology datasets to support grand Challenges on automatic classification of tissue malignancy. The researchers/clinicians responsible for the datasets are:

António Polónia (MD), Ipatimup

Catarina Eloy (MD, PhD), Ipatimup

Paulo Aguiar (PhD), INEB

Tumor classification requires careful examination of hematoxylin and eosin (HE) stained biopsy samples performed by highly specialized pathologists. This is highly time-consuming and costly. Given an image of an HE stain biopsy tissue sample (examples below), the challenge is to automatically classify the sample as: a) normal tissue; b) benign lesion; c) carcinoma in situ; or d) invasive carcinoma.

a) normal tissue	b) benign lesion	c) carcinoma in situ	d) invasive carcinoma

THE DATASETS

Two major datasets have already been produced to support two Grand Challenges of Digital Pathology:

BIOIMAGING 2015 - 4^th International Symposium in Applied Bioimaging [Challenge organized by António Polónia, Catarina Eloy and Paulo Aguiar]

ICIAR 2018 - Grand Challenge on Breast Cancer Histology images [Challenge organized by Teresa Araújo, Guilherme Aresta, António Polónia, Catarina Eloy and Paulo Aguiar]

Please refer to the Grand Challenges webpages for detailed descriptions of the datasets and information on how to get the download credentials.

DOWNLOAD ICIAR'18-BACH CHALLENGE DATASETS

In order for individuals or teams to download the datasets, they need first to register in the Challenge (links above).

Upon registration, credentials for download will be provided. Please abide to the Challenge rules.

THESE DATASETS ARE PUBLICALLY AVAILABLE UNDER A CREATIVE COMMONS CC BY-NC-ND LICENSE (Attribution-NonCommercial-NoDerivs)
ESSENCIALLY, YOU ARE GRANTED ACCESS TO THE DATASETS FOR USE IN YOUR RESEARCH AS LONG AS YOU CREDIT OUR WORK/PUBLICATIONS(*), BUT YOU CANNOT CHANGE THEM IN ANY WAY OR USE THEM COMMERCIALLY

• (*) Aresta, Guilherme, et al. "Bach: Grand challenge on breast cancer histology images." Medical image analysis (2019).
• (*) Araújo, Teresa, et al. "Classification of breast cancer histology images using convolutional neural networks." PloS one 12.6 (2017): e0177544.
• (*) Fondón, Irene, et al. "Automatic classification of tissue malignancy for breast carcinoma diagnosis." Computers in biology and medicine 96 (2018): 41-51.

ICIAR'18-BACH TRAINING DATASET DOWNLOAD >>

ICIAR'18-BACH TEST DATASET DOWNLOAD >>

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