A Cervical Histopathology Dataset for Computer Aided Diagnosis of Precancerous Lesions

Cervical cancer, as one of the most frequently diagnosed cancers worldwide, is curable when detected early. Histopathology images play an important role in precision medicine of the cervical lesions. However, few computer aided algorithms have been explored on cervical histopathology images due to t...

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Bibliographic Details
Published in:IEEE transactions on medical imaging 2021-06, Vol.40 (6), p.1531-1541
Main Authors: Meng, Zhu, Zhao, Zhicheng, Li, Bingyang, Su, Fei, Guo, Limei
Format: Article
Language:English
Subjects:
Algorithms
Annotations
Automation
Benchmarks
Cancer
Cervical cancer
Cervical histopathology
Cervix
Classification
dataset
Datasets
Diagnosis
Feature extraction
Histopathology
Image segmentation
Lesions
Medical diagnosis
Medical imaging
Physicians
Precision medicine
segmentation
Supervised learning
weakly supervised learning
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Summary:Cervical cancer, as one of the most frequently diagnosed cancers worldwide, is curable when detected early. Histopathology images play an important role in precision medicine of the cervical lesions. However, few computer aided algorithms have been explored on cervical histopathology images due to the lack of public datasets. In this article, we release a new cervical histopathology image dataset for automated precancerous diagnosis. Specifically, 100 slides from 71 patients are annotated by three independent pathologists. To show the difficulty of the task, benchmarks are obtained through both fully and weakly supervised learning. Extensive experiments based on typical classification and semantic segmentation networks are carried out to provide strong baselines. In particular, a strategy of assembling classification, segmentation, and pseudo-labeling is proposed to further improve the performance. The Dice coefficient reaches 0.7833, indicating the feasibility of computer aided diagnosis and the effectiveness of our weakly supervised ensemble algorithm. The dataset and evaluation codes are publicly available. To the best of our knowledge, it is the first public cervical histopathology dataset for automated precancerous segmentation. We believe that this work will attract researchers to explore novel algorithms on cervical automated diagnosis, thereby assisting doctors and patients clinically.
ISSN:0278-0062
1558-254X
DOI:10.1109/TMI.2021.3059699