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Fuyong Xing
Researcher at Anschutz Medical Campus
Publications - 92
Citations - 4018
Fuyong Xing is an academic researcher from Anschutz Medical Campus. The author has contributed to research in topics: Convolutional neural network & Segmentation. The author has an hindex of 27, co-authored 79 publications receiving 3074 citations. Previous affiliations of Fuyong Xing include University of Colorado Denver & Rutgers University.
Papers
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Journal ArticleDOI
Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review
Fuyong Xing,Lin Yang +1 more
TL;DR: A comprehensive summary of the recent state-of-the-art nucleus/cell segmentation approaches on different types of microscopy images including bright-field, phase-contrast, differential interference contrast, fluorescence, and electron microscopies is provided.
Journal ArticleDOI
An Automatic Learning-Based Framework for Robust Nucleus Segmentation
Fuyong Xing,Yuanpu Xie,Lin Yang +2 more
TL;DR: A learning-based framework for robust and automatic nucleus segmentation with shape preservation is proposed that is applicable to different staining histopathology images and general enough to perform well across multiple scenarios.
Proceedings ArticleDOI
MDNet: A Semantically and Visually Interpretable Medical Image Diagnosis Network
TL;DR: This paper proposes MDNet to establish a direct multimodal mapping between medical images and diagnostic reports that can read images, generate diagnostic reports, retrieve images by symptom descriptions, and visualize attention, to provide justifications of the network diagnosis process.
Journal ArticleDOI
Deep Learning in Microscopy Image Analysis: A Survey
TL;DR: A snapshot of the fast-growing deep learning field for microscopy image analysis, which explains the architectures and the principles of convolutional neural networks, fully Convolutional networks, recurrent neural Networks, stacked autoencoders, and deep belief networks and their formulations or modelings for specific tasks on various microscopy images.
Journal ArticleDOI
Robust Segmentation of Overlapping Cells in Histopathology Specimens Using Parallel Seed Detection and Repulsive Level Set
TL;DR: This paper proposes a novel algorithm that can reliably separate touching cells in hematoxylin-stained breast TMA specimens that have been acquired using a standard RGB camera and compares the pixel-wise accuracy provided by human experts with that produced by the new automated segmentation algorithm.