Analysis of digital noise reduction methods on classifiers used in automated visual evaluation

Zhiyun Xue; Sandeep Angara; David Levitz; Sameer K. Antani

doi:10.1117/12.2610235

2 March 2022 Analysis of digital noise reduction methods on classifiers used in automated visual evaluation

Zhiyun Xue, Sandeep Angara, David Levitz, Sameer K. Antani

Proceedings Volume 11950, Optics and Biophotonics in Low-Resource Settings VIII; 1195008 (2022) https://doi.org/10.1117/12.2610235
Event: SPIE BiOS, 2022, San Francisco, California, United States

Abstract

Cervical cancer disproportionately hurts underserved women from disadvantaged communities. Automated visual evaluation (AVE), which analyzes white light cervical images using machine learning, is being considered for management of screen-positive patients. Gaussian noise was identified as degrading AVE performance. Two noise correction approaches were tested on images from historic data with added Gaussian noise. One denoising method (VDNet) was based on neural networks; the other used conventional Gaussian blur filtering. Images were evaluated by an object detection network (RetinaNet), and by a binary pathology ResNeSt classifier. VDNet filtering limited AVE performance degradation at higher noise levels, while Guassian blur only worked on low noise levels.

Conference Presentation

Citation Download Citation

Zhiyun Xue, Sandeep Angara, David Levitz, and Sameer K. Antani "Analysis of digital noise reduction methods on classifiers used in automated visual evaluation", Proc. SPIE 11950, Optics and Biophotonics in Low-Resource Settings VIII, 1195008 (2 March 2022); https://doi.org/10.1117/12.2610235

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