  While most previous automation-assisted reading methods can improve
efficiency, their performance often relies on the success of accurate cell
segmentation and hand-craft feature extraction. This paper presents an
efficient and totally segmentation-free method for automated cervical cell
screening that utilizes modern object detector to directly detect cervical
cells or clumps, without the design of specific hand-crafted feature.
Specifically, we use the state-of-the-art CNN-based object detection methods,
YOLOv3, as our baseline model. In order to improve the classification
performance of hard examples which are four highly similar categories, we
cascade an additional task-specific classifier. We also investigate the
presence of unreliable annotations and cope with them by smoothing the
distribution of noisy labels. We comprehensively evaluate our methods on test
set which is consisted of 1,014 annotated cervical cell images with size of
4000*3000 and complex cellular situation corresponding to 10 categories. Our
model achieves 97.5% sensitivity (Sens) and 67.8% specificity (Spec) on
cervical cell image-level screening. Moreover, we obtain a mean Average
Precision (mAP) of 63.4% on cervical cell-level diagnosis, and improve the
Average Precision (AP) of hard examples which are valuable but difficult to
distinguish. Our automation-assisted cervical cell reading method not only
achieves cervical cell image-level classification but also provides more
detailed location and category information of abnormal cells. The results
indicate feasible performance of our method, together with the efficiency and
robustness, providing a new idea for future development of computer-assisted
reading system in clinical cervical screening.
