Deep learning for digital pathology: A critical overview of methodological framework
Deep learning frameworks have transformed the field of digital pathology by automating complex tasks and revealing intricate patterns within histopathological data. These advanced methodologies provide exceptional accuracy and scalability, facilitating the analysis of high-dimensional whole-slide images with unparalleled precision. In this article, we present a comprehensive deep learning framework highlighting recent advancements in computational pathology. We critically examine mathematical innovations and offer a comparative analysis of various models demonstrating the significant and ongoing improvements in the field.
Authors
- Ola El‐Zammar (ORCID: https://orcid.org/0000-0001-9920-0800)
- Michel R. Nasr
- Mohammad Mehdi Hosseini (ORCID: https://orcid.org/0000-0003-4775-761X)
- Tamara Jamaspishvili (ORCID: https://orcid.org/0000-0001-5836-7700)
- Junze Huang (ORCID: https://orcid.org/0000-0001-5304-526X)
- Palak Patel (ORCID: https://orcid.org/0000-0003-4768-8124)
- Maryam Rad (ORCID: https://orcid.org/0000-0002-7927-4893)
- Saverio J. Carello
- Rakesh Choudhary (ORCID: https://orcid.org/0000-0001-9260-348X)
- Bardia Rodd
- Harmen Siezen
- Ratilal Akabari
Institutions
- SUNY Upstate Medical University (US)
- University of Maryland, College Park (US)
- Columbia University (US)
Publication Details
- Journal
- Journal of Pathology Informatics
- Published
- 2025-09-09
- DOI
- https://doi.org/10.1016/j.jpi.2025.100514
- Citations
- 2
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 4.96