Multi-task network for automated analysis of high-resolution endomicroscopy images to detect cervical precancer and cancer

• DOI: 10.1016/j.compmedimag.2022.102052
• Publication Date: 2022-02-27T00:01:40Z
• Authors: David Brenes; CJ Barberan; Brady Hunt; Sonia G. Parra; Mila P. Salcedo; Júlio C. Possati-...; Miriam L. Cremer; Philip E. Castle; José H.T.G. Fregnani; Mauricio Maza; Kathleen M. Schmeler; Richard Baraniuk; Rebecca Richards-...
• Keywords: Papillomavirus Infections; Uterine Cervical Neoplasms; Uterine Cervical Dysplasia; Sensitivity and Specificity; 3. Good health; 03 medical and health sciences; 0302 clinical medicine; Colposcopy; Pregnancy; Humans; Female; Neural Networks, Computer; Early Detection of Cancer

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.