Ms. Francisca Morgado Profile

Francisca Morgado

at NILG.ai LLC

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Proceedings Article | 14 March 2023 Presentation + Paper

Approaches to blur reduction in cervical images for automated visual evaluation

Reuven Weiser, Francisca Morgado, Pedro Dias, Kelwin Fernandes, David Levitz

Proceedings Volume PC12369, PC1236907 (2023) https://doi.org/10.1117/12.2651693

KEYWORDS: Cervix, Visualization, Image quality, Deep learning, Cervical cancer, Image sharpness, Image classification, Data modeling, Education and training, Cancer

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Background: Cervical cancer is a significant burden in many health systems in low and middle income countries (LMICs). Recently, automated visual evaluation (AVE) – using artificial intelligence to analyze cervical images at the point of care (PoC) – has been gaining interest as a new diagnostic test in LMICs. Multiple studies showed that blur (defocus) is the most common challenge to capturing cervical images that are adequate for evaluation by AVE. Methods to reduce blur in cervical images are critical, yet auto-focus functionality degrades when placing an auxiliary lens on a phone. Methods: A cervical image quality analysis algorithm that included blur assessment was developed into an Android application. This algorithm includes an auto-focus module, and secondary blur assessment using deep learning (DL). The auto-focus module was evaluated by bench testing on static cervix images. Two DL approaches (supervised and self-supervised models) were compared against an external dataset. Results and Discussion: A frame by frame analysis on the Samsung J530 and A52, each imaging 3 static images, verified the frame with the least blurry image was selected. The average time for one auto-focus sweep was 8367 ± 630 ms and 7555 ± 146 ms for the J530 and A52, respectively. Within the obstructions detector, the self-supervised model performed better under high blur, with area under the receiver operating characteristic (ROC) curve (AUC) as high as 0.888, while the supervised model performed better with less blur, with ROC AUC values reaching 0.735. To our knowledge, this is the first working targeted auto-focus for cervical imaging.

Proceedings Article | 14 March 2023 Presentation + Paper

Comparison of performance of different automated visual evaluation quality assessment modules

Francisca Morgado, Reuven Weiser, Pedro F. M. Dias, Kelwin Fernandes, David Levitz

Proceedings Volume PC12369, PC1236906 (2023) https://doi.org/10.1117/12.2651687

KEYWORDS: Cervix, Visualization, Image quality, Cervical cancer, Blood, Education and training, Performance modeling, Object detection, Detection and tracking algorithms, Data modeling

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Background: Cervical cancer disproportionally harms women in low and middle income countries (LMICs). There is increasing interest in automated visual evaluation (AVE) – using artificial intelligence to analyze cervical images at the point of care (PoC) – for managing patients in LMICs. AVE has a diagnostic component (for pathology) and a quality component (to ensure image adequacy). The quality component must run on the imaging device at the PoC, and is limited by its processors for computation. Methods: A novel, multiple-module algorithm for assessing cervical image quality was developed in an Android application. One module located the cervix and another sought objects obstructing the transformation zone. The cervix locator module is an object detection model that determined the bounding box of the cervix. Models trained on multiple architectures (YOLOv5 and EfficientDet-Lite2) with the same data were compared. For obstructions classification, a multi-task model was trained to detect 5 common obstructions (blood, SCJ inside of os, loose vaginal walls, mucus, blur/glare) and obstruction-free cervix. Performance of the model’s tasks were compared for 2 different imaging devices. Results and Discussion: The cervix locator performed better and was faster for YOLOv5, although differences were minimal. In the obstructions classifier, 4 different tasks (loose vaginal walls, blood, SCJ inside of os, and obstruction-free) performed satisfactorily. For all modules, the full computation time was <10 sec. Both modules met the desired performance thresholds for image adequacy assessment. The algorithm shown here is to our knowledge, the first AVE quality classifier running on a mobile device.

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