TY - JOUR
T1 - Computational pathology
T2 - A survey review and the way forward
AU - Hosseini, Mahdi S.
AU - Bejnordi, Babak Ehteshami
AU - Trinh, Vincent Quoc Huy
AU - Chan, Lyndon
AU - Hasan, Danial
AU - Li, Xingwen
AU - Yang, Stephen
AU - Kim, Taehyo
AU - Zhang, Haochen
AU - Wu, Theodore
AU - Chinniah, Kajanan
AU - Maghsoudlou, Sina
AU - Zhang, Ryan
AU - Zhu, Jiadai
AU - Khaki, Samir
AU - Buin, Andrei
AU - Chaji, Fatemeh
AU - Salehi, Ala
AU - Nguyen, Bich Ngoc
AU - Samaras, Dimitris
AU - Plataniotis, Konstantinos N.
N1 - Publisher Copyright:
© 2024 The Author(s)
PY - 2024/12
Y1 - 2024/12
N2 - Computational Pathology (CPath) is an interdisciplinary science that augments developments of computational approaches to analyze and model medical histopathology images. The main objective for CPath is to develop infrastructure and workflows of digital diagnostics as an assistive CAD system for clinical pathology, facilitating transformational changes in the diagnosis and treatment of cancer that are mainly address by CPath tools. With evergrowing developments in deep learning and computer vision algorithms, and the ease of the data flow from digital pathology, currently CPath is witnessing a paradigm shift. Despite the sheer volume of engineering and scientific works being introduced for cancer image analysis, there is still a considerable gap of adopting and integrating these algorithms in clinical practice. This raises a significant question regarding the direction and trends that are undertaken in CPath. In this article we provide a comprehensive review of more than 800 papers to address the challenges faced in problem design all-the-way to the application and implementation viewpoints. We have catalogued each paper into a model-card by examining the key works and challenges faced to layout the current landscape in CPath. We hope this helps the community to locate relevant works and facilitate understanding of the field's future directions. In a nutshell, we oversee the CPath developments in cycle of stages which are required to be cohesively linked together to address the challenges associated with such multidisciplinary science. We overview this cycle from different perspectives of data-centric, model-centric, and application-centric problems. We finally sketch remaining challenges and provide directions for future technical developments and clinical integration of CPath. For updated information on this survey review paper and accessing to the original model cards repository, please refer to GitHub. Updated version of this draft can also be found from arXiv.
AB - Computational Pathology (CPath) is an interdisciplinary science that augments developments of computational approaches to analyze and model medical histopathology images. The main objective for CPath is to develop infrastructure and workflows of digital diagnostics as an assistive CAD system for clinical pathology, facilitating transformational changes in the diagnosis and treatment of cancer that are mainly address by CPath tools. With evergrowing developments in deep learning and computer vision algorithms, and the ease of the data flow from digital pathology, currently CPath is witnessing a paradigm shift. Despite the sheer volume of engineering and scientific works being introduced for cancer image analysis, there is still a considerable gap of adopting and integrating these algorithms in clinical practice. This raises a significant question regarding the direction and trends that are undertaken in CPath. In this article we provide a comprehensive review of more than 800 papers to address the challenges faced in problem design all-the-way to the application and implementation viewpoints. We have catalogued each paper into a model-card by examining the key works and challenges faced to layout the current landscape in CPath. We hope this helps the community to locate relevant works and facilitate understanding of the field's future directions. In a nutshell, we oversee the CPath developments in cycle of stages which are required to be cohesively linked together to address the challenges associated with such multidisciplinary science. We overview this cycle from different perspectives of data-centric, model-centric, and application-centric problems. We finally sketch remaining challenges and provide directions for future technical developments and clinical integration of CPath. For updated information on this survey review paper and accessing to the original model cards repository, please refer to GitHub. Updated version of this draft can also be found from arXiv.
KW - Clinical pathology
KW - Computer aided diagnosis (CAD)
KW - Deep learning
KW - Digital pathology
KW - Survey
KW - Whole slide image (WSI)
UR - https://www.scopus.com/pages/publications/85185789321
U2 - 10.1016/j.jpi.2023.100357
DO - 10.1016/j.jpi.2023.100357
M3 - Review article
AN - SCOPUS:85185789321
SN - 2229-5089
VL - 15
JO - Journal of Pathology Informatics
JF - Journal of Pathology Informatics
M1 - 100357
ER -