A Cervical Histopathology Dataset for Computer Aided Diagnosis of Precancerous Lesions
Autor: | Zhu Meng, Bingyang Li, Fei Su, Zhicheng Zhao, Limei Guo |
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Rok vydání: | 2021 |
Předmět: |
medicine.medical_specialty
Computer science Uterine Cervical Neoplasms Machine learning computer.software_genre 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Sørensen–Dice coefficient Image Processing Computer-Assisted medicine Humans Segmentation Diagnosis Computer-Assisted Electrical and Electronic Engineering Cervical cancer Radiological and Ultrasound Technology business.industry medicine.disease Precision medicine Computer Science Applications Computer-aided diagnosis Female Histopathology Artificial intelligence business Precancerous Conditions computer Algorithms Software |
Zdroj: | IEEE Transactions on Medical Imaging. 40:1531-1541 |
ISSN: | 1558-254X 0278-0062 |
Popis: | 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. |
Databáze: | OpenAIRE |
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