Corpus ID: 209445162

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

  title={Comparison-Based Convolutional Neural Networks for Cervical Cell/Clumps Detection in the Limited Data Scenario.},
  author={Yixiong Liang and Zhihong Tang and Meng Yan and Jialin Chen and Qing Liu and Yao Xiang},
  journal={arXiv: Computer Vision and Pattern Recognition},
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… Expand
MACD R-CNN: An Abnormal Cell Nucleus Detection Method
Experiments show that the MACD R-CNN can effectively improve the performance of abnormal cell detection and increase the depth of the convolution layer to further improve the accuracy of cell classification. Expand
Deep Learning in Selected Cancers’ Image Analysis—A Survey
The result of the review process indicated that deep learning methods have achieved state-of-the-art in tumor detection, segmentation, feature extraction and classification. Expand


DeepPap: Deep Convolutional Networks for Cervical Cell Classification
This paper proposes a method to directly classify cervical cells—without prior segmentation—based on deep features, using convolutional neural networks (ConvNets), which outperforms previous algorithms in classification accuracy, area under the curve values, and especially specificity. Expand
DeepCerv: Deep Neural Network for Segmentation Free Robust Cervical Cell Classification
A new deep learning algorithm that does not depend on accurate segmentation by directly classifying image patches with cells is proposed that achieves state of the art accuracy while being extremely fast. Expand
Optimizing the cervix cytological examination based on deep learning and dynamic shape modeling
A robust variational segmentation framework based on superpixelwise convolutional neutral network and a learned shape prior enabling an accurate analysis of overlapping cervical mass is presented, demonstrating that the methodology can successfully segment nuclei and cytoplasm from highly overlapping mass. Expand
Object detection based on deep learning for urine sediment examination
DFPN(Feature Pyramid Network with DenseNet) method to overcome the problem of class confusion in the USE images that it is hard to be solved by baseline model which is the state-of-the-art object detection model FPN with RoIAlign pooling. Expand
Combining fully convolutional networks and graph-based approach for automated segmentation of cervical cell nuclei
A novel approach for segmentation of cervical nuclei that combines fully convolutional networks (FCN) and graph-based approach (FCNG), which is trained to learn the nucleus high-level features to generate a nucleus label mask and a nucleus probabilistic map. Expand
Automatic quantification and classification of cervical cancer via Adaptive Nucleus Shape Modeling
Experiments show that ANSM can achieve an accuracy of 93.33% with a false negative rate of zero in classifying cancer and healthy cervical tissues using nucleus texture features, providing evidence that nucleus-level analysis is valuable in cervical histology image analysis. Expand
Automation‐assisted cervical cancer screening in manual liquid‐based cytology with hematoxylin and eosin staining
  • Ling Zhang, Hui Kong, +4 authors Siping Chen
  • Computer Science, Medicine
  • Cytometry. Part A : the journal of the International Society for Analytical Cytology
  • 2014
The first automation‐assisted system to screen cervical cancer in manual liquid‐based cytology slides with hematoxylin and eosin (H&E) stain is proposed, which is inexpensive and more applicable in developing countries. Expand
An End-to-End System for Automatic Urinary Particle Recognition with Convolutional Neural Network
This paper treats the urinary particle recognition as object detection and exploit two state-of-the-art CNN-based object detection methods, Faster R-CNN and single shot multibox detector (SSD), along with their variants for urinary particle Recognition, and comprehensively evaluates these methods. Expand
Segmentation of Overlapping Cervical Cells in Microscopic Images with Superpixel Partitioning and Cell-Wise Contour Refinement
  • Han S. Lee, Junmo Kim
  • Mathematics, Computer Science
  • 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
  • 2016
An automatic segmentation method for multiple overlapping cervical cells in microscopic images using superpixel partitioning and cell-wise contour refinement and showing competitive performances in two public challenge data sets compared to the state-of-the-art methods. Expand
Segmentation of cytoplasm and nuclei of abnormal cells in cervical cytology using global and local graph cuts
Preliminary validation results obtained from 21 cervical cell images with non-ideal imaging condition and pathology show that the segmentation method achieved 93% accuracy for cytoplasm, and 88.4% F-measure for abnormal nuclei, outperforming state of the art methods in terms of accuracy. Expand