Digital Pathology: Current Issues and Trends
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".
Deadline for manuscript submissions: closed (20 November 2022) | Viewed by 7736
Special Issue Editor
Interests: digital pathology; classical digital image analysis (DIA); deep learning algorithms; neuronal nets; colon cancer; Hirschsprung disease; cytology
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue is devoted to the application of digital pathology (DP) solutions as a key method in pathology and biomarker research. In this Special Issue, we focus on classical image analysis and new approaches using AI-based solutions and deep learning algorithms. This should underline our title “current issues and trends” in digital pathology. In any case, the authors should demonstrate that the part of digital pathology is a predominant and relevant method to get results, and it is direct towards the implementation of new ideas. In our opinion, the participation of experts in the field using leading methods in digital pathology is important to guide readers and DIA users. Papers about non-malignant tissue as well as about malignancies are welcome. When AI solutions are used, details of training and validation sets are welcome and we invite you to upload datasets to supplement data. This can be useful to lead new scientists into the huge field of AI solutions and to demonstrate the power of new approaches that should be the basis of this Special Issue. Particularly welcome will be works that validate, also at the experimental level, data using AI and neuronal nets or deep learning algorithms at any stage in digital pathology.
In situ applications (e.g., FISH or CISH) are reconsidered on the same level as bright-field microscopy. Original papers and case reports are welcome. Your work should encourage young scientists to work with digital pathology solutions and bring DIA one step further into future diagnostics.
Dr. Carol Geppert
Guest Editor
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Keywords
- digital pathology
- digital image analysis
- deep learning
- AI-approach
- AI-based algorithm
- classical image analysis
- digital analysis of immunohistochemistry/biomarkers
- convolutional neural networks (CNNs)
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