  Automation-assisted cervical screening via Pap smear or liquid-based cytology
(LBC) is a highly effective cell imaging based cancer detection tool, where
cells are partitioned into "abnormal" and "normal" categories. However, the
success of most traditional classification methods relies on the presence of
accurate cell segmentations. Despite sixty years of research in this field,
accurate segmentation remains a challenge in the presence of cell clusters and
pathologies. Moreover, previous classification methods are only built upon the
extraction of hand-crafted features, such as morphology and texture. This paper
addresses these limitations by proposing a method to directly classify cervical
cells - without prior segmentation - based on deep features, using
convolutional neural networks (ConvNets). First, the ConvNet is pre-trained on
a natural image dataset. It is subsequently fine-tuned on a cervical cell
dataset consisting of adaptively re-sampled image patches coarsely centered on
the nuclei. In the testing phase, aggregation is used to average the prediction
scores of a similar set of image patches. The proposed method is evaluated on
both Pap smear and LBC datasets. Results show that our method outperforms
previous algorithms in classification accuracy (98.3%), area under the curve
(AUC) (0.99) values, and especially specificity (98.3%), when applied to the
Herlev benchmark Pap smear dataset and evaluated using five-fold
cross-validation. Similar superior performances are also achieved on the HEMLBC
(H&E stained manual LBC) dataset. Our method is promising for the development
of automation-assisted reading systems in primary cervical screening.
