Name
Abstract | ||
|---|---|---|
Automated detection of cervical cancer cells/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 cell patches or to detect cells from the whole image. But the former is computationally expensive, while the latter often requires a large-scale dataset with expensive annotations. In this paper we propose an efficient cervical cancer cells/clumps detection method, called Comparison detector, to deal with the limited data problem. Specifically, we utilize the state-of-the-art proposal-based object detection method, Faster R-CNN with Feature Pyramid Network (FPN) as the baseline and replace the classification of each proposal by comparing it with the prototype representation of each category. In addition, we propose to learn the prototype representation of the background category 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 improved by about 20% comparing to the baseline. Moreover, when training on the medium-sized dataset, our Comparison detector gains a mAP of 48.8% and an AR of 64.0%, improving the AR by 5.1% and the mAP by 3.6% respectively. Our method is promising for the development of automation-assisted cervical cancer screening systems. Code and datasets are available at https://github.com/kuku-sichuan/ComparisonDetector. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.neucom.2021.01.006 | Neurocomputing |
Keywords | DocType | Volume |
Cervical cancer screening,Object detection,Prototype representations,Few-shot learning | Journal | 437 |
ISSN | Citations | PageRank |
0925-2312 | 1 | 0.36 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yixiong Liang | 1 | 5 | 3.41 |
| Zhihong Tang | 2 | 1 | 1.04 |
| Meng Yan | 3 | 1 | 0.70 |
| Jialin Chen | 4 | 1 | 1.04 |
| Qing Liu | 5 | 19 | 3.99 |
| Yao Xiang | 6 | 47 | 5.41 |