Name
Title | ||
|---|---|---|
Comparing Deep Learning Models for Multi-cell Classification in Liquid-based Cervical Cytology Images |
Abstract | ||
|---|---|---|
Liquid-based cytology (LBC) is a reliable automated technique for the screening of Papanicolaou (Pap) smear data. It is an effective technique for collecting a majority of the cervical cells and aiding cytopathologists in locating abnormal cells. Most methods published in the research literature rely on accurate cell segmentation as a prior, which remains challenging due to a variety of factors, e.g., stain consistency, presence of clustered cells, etc. We propose a method for automatic classification of cervical slide images through generation of labeled cervical patch data and extracting deep hierarchical features by fine-tuning convolution neural networks, as well as a novel graph-based cell detection approach for cellular level evaluation. The results show that the proposed pipeline can classify images of both single cell and overlapping cells. The VGG-19 model is found to be the best at classifying the cervical cytology patch data with 95 % accuracy under precision-recall curve. |
Year | Venue | DocType |
|---|---|---|
2019 | AMIA | Conference |
Volume | Citations | PageRank |
2019 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sornapudi Sudhir | 1 | 0 | 0.34 |
| Brown G. T. | 2 | 0 | 0.34 |
| Zhiyun Xue | 3 | 245 | 22.97 |
| L. Rodney Long | 4 | 534 | 56.98 |
| Allen Lisa | 5 | 0 | 0.34 |
| Sameer Antani | 6 | 1402 | 134.03 |