A Spatial Regulated Patch-Wise Approach for Cervical Dysplasia Diagnosis
Keywords:Healthcare, Medicine & Wellness
AbstractCervical dysplasia diagnosis via visual investigation is a challenging problem. Recent approaches use deep learning techniques to extract features and require the downsampling of high-resolution cervical screening images to smaller sizes for training. Such a reduction may result in the loss of visual details that appear weakly and locally within a cervical image. To overcome this challenge, our work divides an image into patches and then represents it from patch features. We aggregate patch patterns into an image feature in a weighted manner by considering the patch--image label relation. The weights are visualized as a heatmap to explain where the diagnosis results come from. We further introduce a spatial regulator to guide the classifier to focus on the cervix region and to adjust the weight distribution, without requiring any manual annotations of the cervix region. A novel iterative algorithm is designed to refine the regulator, which is able to capture the variations in cervix center locations and shapes. Experiments on an 18-year real-world dataset indicate a minimal of 3.47%, 4.59%, 8.54% improvements over the state-of-the-art in accuracy, F1, and recall measures, respectively.
How to Cite
Zhang, Y., Yin, Y., Liu, Z., & Zimmermann, R. (2021). A Spatial Regulated Patch-Wise Approach for Cervical Dysplasia Diagnosis. Proceedings of the AAAI Conference on Artificial Intelligence, 35(1), 733-740. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16154
AAAI Technical Track on Application Domains