Digital Pathology 2.0

A special issue of Biomolecules (ISSN 2218-273X). This special issue belongs to the section "Molecular Medicine".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 4008

Special Issue Editor


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Guest Editor
Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong, China
Interests: cancer biomarker; evidence-based medicine; extracellular vesicles; genomics; microRNA; molecular diagnostics; non-coding RNAs; nasopharyngeal carcinoma; next-generation sequencing; non-small cell lung cancer; proteomics; drug repurposing and bioinformatics
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Special Issue Information

Dear Colleagues,

The invention of the microscope was a milestone for modern medicine and for mankind. Nowadays, disease diagnosis heavily relies on definitive decisions from histopathological analysis of the specimen. In the current era, digital pathology is a dynamic, image-based environment that incorporates the acquisition, management, and interpretation of pathology information generated from a digitized glass slide. Digital slides are created when glass slides are captured by a scanning device, and they provide a high-resolution digital image that can be viewed on a computer screen or mobile device.

Digital pathology can improve the quality of diagnosis in meaningful ways, including reduced errors, improved analysis, and better views. Thus, digital pathology enhances productivity because of the improved workflow, reduced turnaround times, and more innovative design. However, it is also challenging current conventional settings, and the integration of digital pathology should be well planned out.

This Special Issue serves as a platform to propel the field of digital pathology.

The scope of this Special Issue includes, but is not limited to, the following:

  • Virtual multiplex immunohistochemistry;
  • Automated classification of whole-slide images based on deep learning;
  • Super-resolution recurrent convolutional neural networks;
  • Challenges in analysis of digital tissue biopsies;
  • Translational artificial intelligence and deep learning in diagnostic pathology;
  • Efficient algorithms for digital image analysis;
  • Computational pathology, best practices, and recommendations;
  • Sensitivity analysis in digital pathology;
  • Artificial intelligence algorithms in digital pathology;
  • Automated tumor recognition and scoring for biomarkers.

Dr. William Cho
Guest Editor

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Published Papers (1 paper)

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Review

21 pages, 3149 KiB  
Review
Artificial Intelligence Assists in the Detection of Blood Vessels in Whole Slide Images: Practical Benefits for Oncological Pathology
by Anna Timakova, Vladislav Ananev, Alexey Fayzullin, Vladimir Makarov, Elena Ivanova, Anatoly Shekhter and Peter Timashev
Biomolecules 2023, 13(9), 1327; https://doi.org/10.3390/biom13091327 - 29 Aug 2023
Cited by 8 | Viewed by 3363
Abstract
The analysis of the microvasculature and the assessment of angiogenesis have significant prognostic value in various diseases, including cancer. The search for invasion into the blood and lymphatic vessels and the assessment of angiogenesis are important aspects of oncological diagnosis. These features determine [...] Read more.
The analysis of the microvasculature and the assessment of angiogenesis have significant prognostic value in various diseases, including cancer. The search for invasion into the blood and lymphatic vessels and the assessment of angiogenesis are important aspects of oncological diagnosis. These features determine the prognosis and aggressiveness of the tumor. Traditional manual evaluation methods are time consuming and subject to inter-observer variability. Blood vessel detection is a perfect task for artificial intelligence, which is capable of rapid analyzing thousands of tissue structures in whole slide images. The development of computer vision solutions requires the segmentation of tissue regions, the extraction of features and the training of machine learning models. In this review, we focus on the methodologies employed by researchers to identify blood vessels and vascular invasion across a range of tumor localizations, including breast, lung, colon, brain, renal, pancreatic, gastric and oral cavity cancers. Contemporary models herald a new era of computational pathology in morphological diagnostics. Full article
(This article belongs to the Special Issue Digital Pathology 2.0)
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