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Multi-task network for automated analysis of high-resolution endomicroscopy images to detect cervical precancer and cancer.
- Source :
-
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society [Comput Med Imaging Graph] 2022 Apr; Vol. 97, pp. 102052. Date of Electronic Publication: 2022 Feb 26. - Publication Year :
- 2022
-
Abstract
- Cervical cancer is a public health emergency in low- and middle-income countries where resource limitations hamper standard-of-care prevention strategies. The high-resolution endomicroscope (HRME) is a low-cost, point-of-care device with which care providers can image the nuclear morphology of cervical lesions. Here, we propose a deep learning framework to diagnose cervical intraepithelial neoplasia grade 2 or more severe from HRME images. The proposed multi-task convolutional neural network uses nuclear segmentation to learn a diagnostically relevant representation. Nuclear segmentation was trained via proxy labels to circumvent the need for expensive, manually annotated nuclear masks. A dataset of images from over 1600 patients was used to train, validate, and test our algorithm; data from 20% of patients were reserved for testing. An external evaluation set with images from 508 patients was used to further validate our findings. The proposed method consistently outperformed other state-of-the art architectures achieving a test per patient area under the receiver operating characteristic curve (AUC-ROC) of 0.87. Performance was comparable to expert colposcopy with a test sensitivity and specificity of 0.94 (p = 0.3) and 0.58 (p = 1.0), respectively. Patients with recurrent human papillomavirus (HPV) infections are at a higher risk of developing cervical cancer. Thus, we sought to incorporate HPV DNA test results as a feature to inform prediction. We found that incorporating patient HPV status improved test specificity to 0.71 at a sensitivity of 0.94.<br /> (Copyright © 2022 Elsevier Ltd. All rights reserved.)
- Subjects :
- Colposcopy methods
Early Detection of Cancer methods
Female
Humans
Neural Networks, Computer
Pregnancy
Sensitivity and Specificity
Papillomavirus Infections diagnostic imaging
Uterine Cervical Neoplasms diagnostic imaging
Uterine Cervical Neoplasms pathology
Uterine Cervical Dysplasia diagnostic imaging
Uterine Cervical Dysplasia pathology
Subjects
Details
- Language :
- English
- ISSN :
- 1879-0771
- Volume :
- 97
- Database :
- MEDLINE
- Journal :
- Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
- Publication Type :
- Academic Journal
- Accession number :
- 35299096
- Full Text :
- https://doi.org/10.1016/j.compmedimag.2022.102052