  Automated detection of cervical cancer cells or cell clumps has the potential
to significantly reduce error rate and increase productivity in cervical cancer
screening. However, most traditional methods rely on the success of accurate
cell segmentation and discriminative hand-crafted features extraction. Recently
there are emerging deep learning-based methods which train convolutional neural
networks (CNN) to classify image patches, but they are computationally
expensive. In this paper we propose an efficient CNN-based object detection
methods for cervical cancer cells/clumps detection. Specifically, we utilize
the state-of-the-art two-stage object detection method, the Faster-RCNN with
Feature Pyramid Network (FPN) as the baseline and propose a novel comparison
detector to deal with the limited data problem. The key idea is that classify
the proposals by comparing with the reference samples of each category in
object detection. In addition, we propose to learn the reference samples of the
background from data instead of manually choosing them by some heuristic rules.
Experimental results show that the proposed Comparison Detector yields
significant improvement on the small dataset, achieving a mean Average
Precision (mAP) of 26.3% and an Average Recall (AR) of 35.7%, both improving
about 20 points compared to the baseline. Moreover, Comparison Detector
improved AR by 4.6 points and achieved marginally better performance in terms
of mAP compared with baseline model when training on the medium dataset. Our
method is promising for the development of automation-assisted cervical cancer
screening systems. Code is available at
https://github.com/kuku-sichuan/ComparisonDetector.
