Comparison-Based Convolutional Neural Networks for Cervical Cell/Clumps Detection in the Limited Data Scenario

Abstract

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.

References in corpus (11)

• A Survey on Deep Learning in Medical Image Analysis
• Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
• YOLOv3: An Incremental Improvement
• Prototypical Networks for Few-shot Learning
• Achieving Human Parity in Conversational Speech Recognition
• Deformable Convolutional Networks
• One-shot Learning with Memory-Augmented Neural Networks
• Light-Head R-CNN: In Defense of Two-Stage Object Detector
• Deep Learning for Generic Object Detection: A Survey
• Few-Example Object Detection with Model Communication
• Considerations for a PAP Smear Image Analysis System with CNN Features