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

David Brenes, C. J. Barberan, Brady Hunt, Sonia G. Parra, Mila P. Salcedo, Júlio C. Possati-Resende, Miriam L. Cremer, Philip E. Castle, José H.T.G. Fregnani, Mauricio Maza, Kathleen M. Schmeler, Richard Baraniuk, Rebecca Richards-Kortum

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


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.

Original languageEnglish (US)
Article number102052
JournalComputerized Medical Imaging and Graphics
StatePublished - Apr 2022


  • Cervical precancer
  • Endomicroscopy
  • Multi-task learning
  • Point-of-care

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Health Informatics
  • Computer Graphics and Computer-Aided Design


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