Deep Learning Approaches in Histopathology
The revolution of artificial intelligence and its impacts on our daily life has led to tremendous interest in the field and its related subtypes: machine learning and deep learning. Scientists and developers have designed machine learning- and deep learning-based algorithms to perform various tasks...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-10-01
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| Series: | Cancers |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-6694/14/21/5264 |
| _version_ | 1797468879998418944 |
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| author | Alhassan Ali Ahmed Mohamed Abouzid Elżbieta Kaczmarek |
| author_facet | Alhassan Ali Ahmed Mohamed Abouzid Elżbieta Kaczmarek |
| author_sort | Alhassan Ali Ahmed |
| collection | DOAJ |
| description | The revolution of artificial intelligence and its impacts on our daily life has led to tremendous interest in the field and its related subtypes: machine learning and deep learning. Scientists and developers have designed machine learning- and deep learning-based algorithms to perform various tasks related to tumor pathologies, such as tumor detection, classification, grading with variant stages, diagnostic forecasting, recognition of pathological attributes, pathogenesis, and genomic mutations. Pathologists are interested in artificial intelligence to improve the diagnosis precision impartiality and to minimize the workload combined with the time consumed, which affects the accuracy of the decision taken. Regrettably, there are already certain obstacles to overcome connected to artificial intelligence deployments, such as the applicability and validation of algorithms and computational technologies, in addition to the ability to train pathologists and doctors to use these machines and their willingness to accept the results. This review paper provides a survey of how machine learning and deep learning methods could be implemented into health care providers’ routine tasks and the obstacles and opportunities for artificial intelligence application in tumor morphology. |
| first_indexed | 2024-03-09T19:13:37Z |
| format | Article |
| id | doaj.art-c61a2fc3f1f34452b0f46c5c7c0b02b9 |
| institution | Directory Open Access Journal |
| issn | 2072-6694 |
| language | English |
| last_indexed | 2024-03-09T19:13:37Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cancers |
| spelling | doaj.art-c61a2fc3f1f34452b0f46c5c7c0b02b92023-11-24T04:01:39ZengMDPI AGCancers2072-66942022-10-011421526410.3390/cancers14215264Deep Learning Approaches in HistopathologyAlhassan Ali Ahmed0Mohamed Abouzid1Elżbieta Kaczmarek2Department of Bioinformatics and Computational Biology, Poznan University of Medical Sciences, 60-812 Poznan, PolandDoctoral School, Poznan University of Medical Sciences, 60-812 Poznan, PolandDepartment of Bioinformatics and Computational Biology, Poznan University of Medical Sciences, 60-812 Poznan, PolandThe revolution of artificial intelligence and its impacts on our daily life has led to tremendous interest in the field and its related subtypes: machine learning and deep learning. Scientists and developers have designed machine learning- and deep learning-based algorithms to perform various tasks related to tumor pathologies, such as tumor detection, classification, grading with variant stages, diagnostic forecasting, recognition of pathological attributes, pathogenesis, and genomic mutations. Pathologists are interested in artificial intelligence to improve the diagnosis precision impartiality and to minimize the workload combined with the time consumed, which affects the accuracy of the decision taken. Regrettably, there are already certain obstacles to overcome connected to artificial intelligence deployments, such as the applicability and validation of algorithms and computational technologies, in addition to the ability to train pathologists and doctors to use these machines and their willingness to accept the results. This review paper provides a survey of how machine learning and deep learning methods could be implemented into health care providers’ routine tasks and the obstacles and opportunities for artificial intelligence application in tumor morphology.https://www.mdpi.com/2072-6694/14/21/5264artificial intelligenceimage analysisdeep learningmachine learningpathologytumor morphology |
| spellingShingle | Alhassan Ali Ahmed Mohamed Abouzid Elżbieta Kaczmarek Deep Learning Approaches in Histopathology Cancers artificial intelligence image analysis deep learning machine learning pathology tumor morphology |
| title | Deep Learning Approaches in Histopathology |
| title_full | Deep Learning Approaches in Histopathology |
| title_fullStr | Deep Learning Approaches in Histopathology |
| title_full_unstemmed | Deep Learning Approaches in Histopathology |
| title_short | Deep Learning Approaches in Histopathology |
| title_sort | deep learning approaches in histopathology |
| topic | artificial intelligence image analysis deep learning machine learning pathology tumor morphology |
| url | https://www.mdpi.com/2072-6694/14/21/5264 |
| work_keys_str_mv | AT alhassanaliahmed deeplearningapproachesinhistopathology AT mohamedabouzid deeplearningapproachesinhistopathology AT elzbietakaczmarek deeplearningapproachesinhistopathology |