Comparing Deep Learning Models for Multi-cell Classification in Liquid-based Cervical Cytology Images
This work addresses the challenge of automating cervical cancer screening for cytopathologists by improving classification accuracy without needing precise segmentation, though it appears incremental as it builds on existing deep learning methods.
The paper tackled the problem of classifying cervical cytology images without relying on accurate cell segmentation by proposing a pipeline that uses deep hierarchical features and a graph-based cell detection approach. The result was that the VGG-19 model achieved 95% accuracy under the precision-recall curve for classifying both single and overlapping cells.
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.