Abstract:
This paper describes our automatic cell image classification algorithm that explores expert's eye tracking data combined to convolutional neural networks. Our framework s...Show MoreMetadata
Abstract:
This paper describes our automatic cell image classification algorithm that explores expert's eye tracking data combined to convolutional neural networks. Our framework selects regions of interest that attract cytologists attention, then it focuses computation on cell classification of these specific sub-images. Our contribution is to fuse deep learning to saliency maps from eye-tracking into an approach that bypasses segmentation to detect abnormal cells from Pap smear microscopy under real noisy conditions, artifacts and occlusion. Preliminary results show high classification accuracy of ~90% during tasks of locating and identifying critical cells within three levels: normal, low-risk disease and high-risk disease. We validate our results on 111 images containing 3,183 cells and obtained an average runtime of 4.5 seconds per image.
Date of Conference: 08-11 April 2019
Date Added to IEEE Xplore: 11 July 2019
ISBN Information: