Deep Voting and Structured Regression for Microscopy Image Analysis
Yuanpu Xie; Fuyong Xing; Lin Yang University of Florida, Gainesville, FL, United States
Abstract
Robust and accurate nuclei localization in microscopy images can provide crucial clues for accurate computer-aided diagnosis. In this chapter, we present two methods that rely on convolutional neural networks (CNNs) to solve this problem. The first one is named as deep voting, which is a CNN based hough voting method used to localize nucleus centroids that exhibit heavy cluttering and morphological variations. It mainly consists of the following two parts: (i) Given an input image, this model maps every local testing image patch to the proposed target information ...
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