- Abstract: Automating cervical cancer screening has the potential to reduce high mortality rates due to cervical cancer, especially in developing countries. The most promising of these techniques is assisted screening in which preliminary analysis is validated by a pathologist. Assisted screening requires classification algorithms for initial screening that is later validated by a pathologist. It also needs attention, detection or segmentation algorithms for drawing attention of pathologists to important regions. Existing algorithms for cervical cancer screening focus on classification of individual cells . This focus leads to need for accurate segmentation of cells and inability to use extracellular information. In this work we propose a segmentation free deep learning algorithm for classification of PAP smear images. The proposed algorithm uses the intrinsic information in the network to generate a map of important regions for the pathologist to look into. This map is generated with sub image resolution while the training data contains annotations at the image level only. Our analysis on a dataset of around 14000 images validates the approach of assisted screening in reducing the pathologist workload by a large factor.
- Keywords: Cervical cancer, Papanicolaou test, Assistive screening
- Author Affiliation: Aindra Systems