Digital imaging in pathology: whole-slide imaging and beyond.

By Farzad Ghaznavi, A. Evans, Anant Madabhushi, Michael D. Feldman. Annual Review of Pathology, 2013

449 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biophysics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/33efb8cfcc08fd98e4a5954c49b60e4cee431d97

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Citations in the corpus

Applications in image-based profiling of perturbations. (2016, background): “We focus here on applications that involve systematically profiling large numbers of perturbations interrogated by microscopy imaging; outside this scope are other important applications such as high-throughput image-based screening (Box 1), pathology applications involving human tissue samples [10]”
Automated Gleason grading of prostate cancer tissue microarrays via deep learning (2018, background): “Automated computational approaches operating on digital pathology images bear the potential to overcome the above-mentioned limitations, deliver reproducible results and achieve high throughput by multiplexing of computational resources 7 .”
Methods for Segmentation and Classification of Digital Microscopy Tissue Images (2018, background): “Methods for segmentation and classification have been proposed by several research projects (Gurcan et al., 2009; Ghaznavi et al., 2013; Xie et al., 2015; Xu et al., 2015; Manivannan et al., 2016; Peikari and Martel, 2016; Sirinukunwattana et al., 2016; Wang et al., 2016; Xing and Yang, 2016; Al-Mil”
Breast Cancer Histopathology Image Analysis: A Review (2014, unknown): “For a broader overview of digital pathology and the use of automatic methods for analysis of histopathology slides, we refer the reader to the recent reviews in [11]–[15].”
Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks (2014, background): “Digital pathology is one of the most recent instances of big electronic data and comes from the digitalization process of histopathology glass slides by digital scanners.(20) The digitized whole slide pathology images are often several gigabytes in size.”
Artificial Intelligence-Based Breast Cancer Nodal Metastasis Detection: Insights Into the Black Box for Pathologists. (2018, background): “Many other works using machine learning or deep learning for breast and other malignancies have been published.(9,10) Moreover, deep learning–based algorithms accurately detect metastatic breast cancer in lymph nodes, based on both slide-level and tumor-level receiveroperating-characteristic perform”
Stain Specific Standardization of Whole-Slide Histopathological Images (2016, background): “Tissue sections are stained with multiple contrasting dyes to highlight different tissue structures and cellular features [1].”