Name
Title | ||
|---|---|---|
Multi-task network for automated analysis of high-resolution endomicroscopy images to detect cervical precancer and cancer |
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.compmedimag.2022.102052 | COMPUTERIZED MEDICAL IMAGING AND GRAPHICS |
Keywords | DocType | Volume |
Endomicroscopy, Cervical precancer, Multi-task learning, Point-of-care | Journal | 97 |
ISSN | Citations | PageRank |
0895-6111 | 0 | 0.34 |
References | Authors | |
0 | 13 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| David Brenes | 1 | 0 | 0.34 |
| C J Barberan | 2 | 0 | 0.34 |
| Brady Hunt | 3 | 0 | 0.34 |
| Sonia G Parra | 4 | 0 | 0.34 |
| Mila P Salcedo | 5 | 0 | 0.34 |
| Júlio C Possati-Resende | 6 | 0 | 0.34 |
| Miriam L Cremer | 7 | 0 | 0.34 |
| Philip E Castle | 8 | 0 | 0.34 |
| José H T G Fregnani | 9 | 0 | 0.34 |
| Mauricio Maza | 10 | 0 | 0.34 |
| Kathleen M Schmeler | 11 | 0 | 0.34 |
| Richard G. Baraniuk | 12 | 5053 | 489.23 |
| Rebecca Richards-Kortum | 13 | 22 | 5.12 |