Improving Whole Slide Segmentation Through Visual Context - A Systematic Study
While challenging, the dense segmentation of histology images is a necessary first step to assess changes in tissue architecture and cellular morphology. Although specific convolutional neural network architectures have been applied with great success to the problem, few effectively incorporate visual context information from multiple scales. With this paper, we present a systematic comparison of different architectures to assess how including multi-scale information affects segmentation performance. A publicly available breast cancer and a locally collected prostate cancer datasets are being utilised for this study. The results support our hypothesis that visual context and scale plays a crucial role in histology image classification problems.
KeywordsDigital pathology Whole slide imaging Dense segmentation Deep learning
This research was funded by the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC) and the EPSRC SeeBiByte Programme Grant (EP/M013774/1). The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health.
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