Cervical cancer detection in cervical smear images using deep pyramid inference with refinement and spatial‐aware booster

Abstract

With the development of artificial intelligence and image processing technology, more and more intelligent diagnosis technologies are used in cervical cancer screening. Among them, the detection of cervical lesions by thin liquid‐based cytology is the most common method for cervical cancer screening. At present, most cervical cancer detection algorithms use the object detection technology of natural images, and often only minor modifications are made while ignoring the specificity of the complex application scenario of cervical lesions detection in cervical smear images. In this study, the authors combine the domain knowledge of cervical cancer detection and the characteristics of pathological cells to design a network and propose a booster for cervical cancer detection (CCDB). The booster mainly consists of two components: the refinement module and the spatial‐aware module. The characteristics of cancer cells are fully considered in the booster, and the booster is light and transplantable. As far as the authors know, they are the first to design a CCDB according to the characteristics of cervical cancer cells. Compared with baseline (Retinanet), the sensitivity at four false positives per image and average precision of the proposed method are improved by 2.79 and 7.2%, respectively.

Detection of Cervical Cancer Cells in Whole Slide Images Us-ing Deformable and Global Context Aware Faster RCNN-FPN

“…The most important findings of our study are as follows: (1). There are currently few network architectures designed specifically for cervical cancer cell detection in Pap smear images [42,[44][45][46]. Based on one of the currently top-performing detectors faster RCCN-FPN [57]…”

“…The cells have been manually classified into five cell types without pre-neoplastic alteration by expert cytopathologists. Some previous studies have relied on image collections of cut-out images [41,43] of clean images or positive samples [42]. Despite of the seemingly good performance, these results are not so realistic and clinically relevant.…”

“…Tan et al [44] used also Faster RCNN architecture for cervical cancer cell detection in ThinPrep cytologic test (TCT) images scanned with a seamless slider at up to 400× and achieved AUC of 0.67. More recently, Ma et al [42] proposed an improved Faster RCNN-FPN architecture for cervical cancer detection in cropped patches out of positive Pap smear images. They designed a lightweight booster consisting of a refinement and spatial-aware module, aimed to enhance feature details and spatial context information.…”

Detection of Cervical Cancer Cells in Whole Slide Images Using Deformable and Global Context Aware Faster RCNN-FPN

“…The object detection methods have also begun to find applications for cervical cancer detection in cervical cytology images. At present, there are few network architectures designed specifically for the specific task of cervical cancer detection [ 41 , 42 , 43 , 44 , 45 , 46 ]. Xu et al [ 45 ] used the generic Faster RCNN for the detection of abnormal cells in cervical smear images scanned at 20× and showed that detection of various abnormal cells was feasible.…”

“…Tan et al [ 44 ] also used Faster RCNN architecture for cervical cancer cell detection in ThinPrep cytologic test (TCT) images scanned with a seamless slider at up to 400× and achieved AUC of 0.67. More recently, Ma et al [ 42 ] proposed an improved Faster RCNN-FPN architecture for cervical cancer detection in cropped patches out of positive Pap smear images. They designed a lightweight booster consisting of a refinement and spatial-aware module, aimed to enhance feature details and spatial context information.…”