The State of Applying Artificial Intelligence to Tissue Imaging for Cancer Research and Early Detection
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arXiv
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| Format: | Preprint |
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2023
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| author | Robben, Michael Hajighasemi, Amir Nasr, Mohammad Sadegh Veerla, Jai Prakesh Alsup, Anne M. Rout, Biraaj Shang, Helen H. Fowlds, Kelli Malidarreh, Parisa Boodaghi Koomey, Paul Saurav, MD Jillur Rahman Luber, Jacob M. |
| author_facet | Robben, Michael Hajighasemi, Amir Nasr, Mohammad Sadegh Veerla, Jai Prakesh Alsup, Anne M. Rout, Biraaj Shang, Helen H. Fowlds, Kelli Malidarreh, Parisa Boodaghi Koomey, Paul Saurav, MD Jillur Rahman Luber, Jacob M. |
| contents | Artificial intelligence represents a new frontier in human medicine that could save more lives and reduce the costs, thereby increasing accessibility. As a consequence, the rate of advancement of AI in cancer medical imaging and more particularly tissue pathology has exploded, opening it to ethical and technical questions that could impede its adoption into existing systems. In order to chart the path of AI in its application to cancer tissue imaging, we review current work and identify how it can improve cancer pathology diagnostics and research. In this review, we identify 5 core tasks that models are developed for, including regression, classification, segmentation, generation, and compression tasks. We address the benefits and challenges that such methods face, and how they can be adapted for use in cancer prevention and treatment. The studies looked at in this paper represent the beginning of this field and future experiments will build on the foundations that we highlight. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_16989 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | The State of Applying Artificial Intelligence to Tissue Imaging for Cancer Research and Early Detection Robben, Michael Hajighasemi, Amir Nasr, Mohammad Sadegh Veerla, Jai Prakesh Alsup, Anne M. Rout, Biraaj Shang, Helen H. Fowlds, Kelli Malidarreh, Parisa Boodaghi Koomey, Paul Saurav, MD Jillur Rahman Luber, Jacob M. Tissues and Organs Computer Vision and Pattern Recognition Image and Video Processing Artificial intelligence represents a new frontier in human medicine that could save more lives and reduce the costs, thereby increasing accessibility. As a consequence, the rate of advancement of AI in cancer medical imaging and more particularly tissue pathology has exploded, opening it to ethical and technical questions that could impede its adoption into existing systems. In order to chart the path of AI in its application to cancer tissue imaging, we review current work and identify how it can improve cancer pathology diagnostics and research. In this review, we identify 5 core tasks that models are developed for, including regression, classification, segmentation, generation, and compression tasks. We address the benefits and challenges that such methods face, and how they can be adapted for use in cancer prevention and treatment. The studies looked at in this paper represent the beginning of this field and future experiments will build on the foundations that we highlight. |
| title | The State of Applying Artificial Intelligence to Tissue Imaging for Cancer Research and Early Detection |
| topic | Tissues and Organs Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2306.16989 |