A histopathological image dataset for endometrial disease diagnosis

Recent
studies have shown that computer-aided diagnosis (CADx) systems based on deep learning
can perform even better than human experts in detecting some common diseases,
such as diabetic retinopathy and pneumonia. However, the scarcity of labeled
medical imaging data impedes a broad application of this technique to diagnosis of other malignant tumors that receive
relatively little attention, especially for women. For example, uterine cancer
(also known as endometrial cancer) can seriously affect the female reproductive
organs, but it receives less attention than breast cancer and lung cancer. We developed
a histopathological image dataset of the endometrium to help alleviate the shortage
of pathologically proven cases in using deep learning algorithms to train classification
models for endometrial diseases. The dataset comprises ~3,300 joint-photographic-experts-group
(JPEG) images from ~500 endometrial specimens stained with hematoxylin and
eosin (H&E), each of which has a unique class label derived from the
corresponding diagnosis made by three expert pathologists collectively. Note
that we will continue to enrich this dataset in the future. All the related source code is
available at https://github.com/ssea-lab/DL4ETI.