Stacked sparse autoencoder and case-based postprocessing method for nucleus detection
Abstract
Accurate nucleus detection is of great importance in pathological image analyses and diagnoses, which is a critical prerequisite for tasks such as automated grading hepatocellular carcinoma (HCC) nuclei. This paper proposes an automated nucleus detection framework based on a stacked sparse autoencoder (SSAE) and a case-based postprocessing method (CPM) in a coarse-to-fine manner. SSAE, an unsupervised learning model, is first trained using image...
Paper Details
Title
Stacked sparse autoencoder and case-based postprocessing method for nucleus detection
Published Date
Sep 1, 2019
Journal
Volume
359
Pages
494 - 508
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