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Journal of Pathology Informatics is a peer-reviewed journal published by Elsevier, covering AI in cancer detection, Cell Image Analysis Techniques, Radiomics and Machine Learning in Medical Imaging.
| Publisher | Elsevier |
|---|---|
| Reference style | Numbered (Elsevier) Numbered — [1], [2] in the text [1] A. Smith, B. Jones, C. Lee, A representative article title, Journal of Pathology Informatics 12 (2023) 45–58.
Formats any DOI in Journal of Pathology Informatics style. No sign-up. |
| Publishes research in | AI in cancer detection Cell Image Analysis Techniques Radiomics and Machine Learning in Medical Imaging Digital Imaging for Blood Diseases Clinical Laboratory Practices and Quality Control |
| ISSN | 2153-3539 |
| Citation impact (2-yr) | 2.97 |
| h-index | 68 |
| i10-index | 503 |
| Total citations | 23,273 |
| Article processing charge | $1,400 |
| Open access | Yes |
| Top institutions publishing here | University of Pittsburgh Medical Center |
| Journal website | www.journals.elsevier.com |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
BACKGROUND: Deep learning (DL) is a representation learning approach ideally suited for image analysis challenges in digital pathology (DP). The variety of image analysis tasks in the context of DP includes detection and counting (e.g., mitotic events), segmentation (e.g., nuclei), and tissue classification (e.g., cancerous vs. non-cancerous). Unfortunately, issues with slide preparation, variations in staining…
Although widely touted as a replacement for glass slides and microscopes in pathology, digital slides present major challenges in data storage, transmission, processing and interoperability. Since no universal data format is in widespread use for these images today, each vendor defines its own proprietary data formats, analysis tools, viewers and software libraries. This creates issues…
In light of the recent success of artificial intelligence (AI) in computer vision applications, many researchers and physicians expect that AI would be able to assist in many tasks in digital pathology. Although opportunities are both manifest and tangible, there are clearly many challenges that need to be overcome in order to exploit the AI…
Whole slide imaging (WSI), or "virtual" microscopy, involves the scanning (digitization) of glass slides to produce "digital slides". WSI has been advocated for diagnostic, educational and research purposes. When used for remote frozen section diagnosis, WSI requires a thorough implementation period coupled with trained support personnel. Adoption of WSI for rendering pathologic diagnoses on a…
The advent of whole-slide imaging in digital pathology has brought about the advancement of computer-aided examination of tissue via digital image analysis. Digitized slides can now be easily annotated and analyzed via a variety of algorithms. This study reviews the fundamentals of tissue image analysis and aims to provide pathologists with basic information regarding the…
