Computational Pathology: A Survey Review and The Way Forward
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866929225493118976 |
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| author | Hosseini, Mahdi S. Bejnordi, Babak Ehteshami Trinh, Vincent Quoc-Huy Hasan, Danial Li, Xingwen Kim, Taehyo Zhang, Haochen Wu, Theodore Chinniah, Kajanan Maghsoudlou, Sina Zhang, Ryan Yang, Stephen Zhu, Jiadai Chan, Lyndon Khaki, Samir Buin, Andrei Chaji, Fatemeh Salehi, Ala Nguyen, Bich Ngoc Samaras, Dimitris Plataniotis, Konstantinos N. |
| author_facet | Hosseini, Mahdi S. Bejnordi, Babak Ehteshami Trinh, Vincent Quoc-Huy Hasan, Danial Li, Xingwen Kim, Taehyo Zhang, Haochen Wu, Theodore Chinniah, Kajanan Maghsoudlou, Sina Zhang, Ryan Yang, Stephen Zhu, Jiadai Chan, Lyndon Khaki, Samir Buin, Andrei Chaji, Fatemeh Salehi, Ala Nguyen, Bich Ngoc Samaras, Dimitris Plataniotis, Konstantinos N. |
| contents | 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 (https://github.com/AtlasAnalyticsLab/CPath_Survey). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_05482 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Computational Pathology: A Survey Review and The Way Forward Hosseini, Mahdi S. Bejnordi, Babak Ehteshami Trinh, Vincent Quoc-Huy Hasan, Danial Li, Xingwen Kim, Taehyo Zhang, Haochen Wu, Theodore Chinniah, Kajanan Maghsoudlou, Sina Zhang, Ryan Yang, Stephen Zhu, Jiadai Chan, Lyndon Khaki, Samir Buin, Andrei Chaji, Fatemeh Salehi, Ala Nguyen, Bich Ngoc Samaras, Dimitris Plataniotis, Konstantinos N. Image and Video Processing Computer Vision and Pattern Recognition 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 (https://github.com/AtlasAnalyticsLab/CPath_Survey). |
| title | Computational Pathology: A Survey Review and The Way Forward |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2304.05482 |