Cervical glandular cell (GC) detection is a key step in computer-aided
diagnosis for cervical adenocarcinomas screening. It is challenging to
accurately recognize GCs in cervical smears in which squamous cells are the
major. Widely existing Out-Of-Distribution (OOD) data in the entire smear leads
decreasing reliability of machine learning system for GC detection. Although,
the State-Of-The-Art (SOTA) deep learning model can outperform pathologists in
preselected regions of interest, the mass False Positive (FP) prediction with
high probability is still unsolved when facing such gigapixel whole slide
image. This paper proposed a novel PolarNet based on the morphological prior
knowledge of GC trying to solve the FP problem via a self-attention mechanism
in eight-neighbor. It estimates the polar orientation of nucleus of GC. As a
plugin module, PolarNet can guide the deep feature and predicted confidence of
general object detection models. In experiments, we discovered that general
models based on four different frameworks can reject FP in small image set and
increase the mean of average precision (mAP) by 0.007∼0.015
in average, where the highest exceeds the recent cervical cell detection model
0.037. By plugging PolarNet, the deployed C++ program improved by 8.8\% on
accuracy of top-20 GC detection from external WSIs, while sacrificing 14.4 s of
computational time. Code is available in
https://github.com/Chrisa142857/PolarNet-GCdetComment: 11 pages, 9 figure