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 $\text{0.007}\sim\text{0.015}$ 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-GCdet
翻译:虽然州-州-州-州-州(SOTA)深层次学习模型可以比预选目标检测模型的深度特征和预测信任度强得多,但是,在预选区域,高概率的大规模假阳性(FP)预测在面临这种千兆升整张幻灯片图像时仍无法解析。本文建议根据GC先前通过八度内邻bor的自我保存机制试图解决FP问题的形态学知识建立一个全新的极地网。它估计了GC检测的机器学习系统的极地方向。作为一个插件模块,极地网可以指导普通物体检测模型的深度特征和预测信任度。在实验中,基于四个不同框架的大规模假正值预测在面对这样的千兆头整张幻灯片图像时仍无法解析。本文建议基于GC先前试图通过八度内邻的自我保存机制解决FPM问题的变异态知识的新的极地网。