Pathology imaging informatics for quantitative analysis of whole-slide images.
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TLDR
This article provides a thorough review of current methods for histopathological whole-slide imaging informatics methods, associated challenges, and future research opportunities and presents a case study to illustrate a clinical decision support system that begins with quality control and ends with predictive modeling for several cancer endpoints.About:
This article is published in Journal of the American Medical Informatics Association.The article was published on 2013-11-01 and is currently open access. It has received 243 citations till now. The article focuses on the topics: Imaging informatics & Informatics.read more
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Detecting Cancer Metastases on Gigapixel Pathology Images
Yun Liu,Krishna Gadepalli,Mohammad Norouzi,George E. Dahl,Timo Kohlberger,Subhashini Venugopalan,Aleksey S Boyko,Aleksei Timofeev,Philip Q Nelson,Greg S. Corrado,Jason D. Hipp,Lily Peng,Martin C. Stumpe +12 more
TL;DR: This work presents a framework to automatically detect and localize tumors as small as 100 x 100 pixels in gigapixel microscopy images sized 100,000 x100,000 pixels and achieves image-level AUC scores above 97% on both the Camelyon16 test set and an independent set of 110 slides.
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.
Proceedings ArticleDOI
Nuclei Segmentation with Recurrent Residual Convolutional Neural Networks based U-Net (R2U-Net)
TL;DR: In this implementation, the R2U-Net is applied to nuclei segmentation for the first time on a publicly available dataset that was collected from the Data Science Bowl Grand Challenge in 2018.
Journal ArticleDOI
Neutrophils dominate the immune cell composition in non-small cell lung cancer
Julia Kargl,Stephanie E. Busch,Grace H. Y. Yang,Kyoung-Hee Kim,Mark L. Hanke,Heather E. Metz,Jesse J. Hubbard,Sylvia Lee,David K. Madtes,Martin W. McIntosh,A. McGarry Houghton +10 more
TL;DR: The results show that the immune cell composition is fundamentally different in lung adenocarcinoma as compared with lung squamous cell carcinoma, and that neutrophils are the most prevalent immune cell type.
Journal ArticleDOI
Deep convolutional neural networks for automatic classification of gastric carcinoma using whole slide images in digital histopathology
TL;DR: This study explores deep learning methods for computer-aided classification in H&E stained histopathological whole slide images of gastric carcinoma using the widely known AlexNet deep convolutional framework.
References
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Towards a knowledge-based Human Protein Atlas
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TL;DR: The analysis here suggests that state stem cell funding programs are sufficiently large and established that simply ending the programs, at least in the absence of substantial investment in the field by other funding sources, could have deleterious effects.
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Review of shape representation and description techniques
Dengsheng Zhang,Guojun Lu +1 more
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Histopathological Image Analysis: A Review
TL;DR: The recent state of the art CAD technology for digitized histopathology is reviewed and the development and application of novel image analysis technology for a few specific histopathological related problems being pursued in the United States and Europe are described.