Artificial Intelligence (AI) and Machine Learning (ML) in Medical Imaging Informatics towards Diagnostic Decision Making
In recent years, AI/ML tools have become more prevalent in the fields of medical imaging and imaging informatics, where systems are already outperforming physicians in a range of domains, such as in the classification of retinal fundus images in ophthalmology, chest X-rays in radiology, and skin can...
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| Format: | Online |
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| Jezik: | engleski |
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MDPI - Multidisciplinary Digital Publishing Institute
2023
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| Online pristup: | ONIX_20230808_9783036581286_25 |
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| collection | Directory of Open Access Books |
| description | In recent years, AI/ML tools have become more prevalent in the fields of medical imaging and imaging informatics, where systems are already outperforming physicians in a range of domains, such as in the classification of retinal fundus images in ophthalmology, chest X-rays in radiology, and skin cancer detection in dermatology, among many others. It has recently emerged as one of the fastest growing research areas given the evolution of techniques in radiology, molecular imaging, anatomical imaging, and functional imaging for detection, segmentation, diagnosis, annotation, summarization, and prediction. The ongoing innovations in this exciting and promising field play a powerful role in influencing the lives of millions through health, safety, education, and other opportunities intended to be shared across all segments of society. To achieve further progress, this Special Issue (SI) invited both research and review-type manuscripts to showcase ongoing research progress and development based on applications of AI/ML (especially DL techniques) in medical imaging to influence human health and healthcare systems in the diagnostic decision-making process. The SI published fourteen articles after a rigorous peer-review process across the spectrum of medical imaging modalities and the diversity of specialties depending on imaging techniques from radiology, dermatology, pathology, colonoscopy, endoscopy, etc. |
| format | Online |
| id | doab-20.500.12854ir-112457 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1124572024-03-31T13:10:10Z Artificial Intelligence (AI) and Machine Learning (ML) in Medical Imaging Informatics towards Diagnostic Decision Making Rahman, Mahmudur machine learning feature selection osteoporosis postmenopausal women pre-screening risk assessment colorectal cancer colon polyp image features convolutional neural network artificial intelligence radiomics pancreatic imaging MRI CT PET acute lymphoblastic leukemia (ALL) blood smear convolutional neural networks deep learning white blood cells dysarthria gated recurrent units ordinal classification multi-instance learning weak supervision breast cancer key instance uncertainty select mammography deep neural network classification HAM10000 skin lesion ESRGAN medical imaging healthcare decision making cervical cancer ensemble learning Internet of Medical Things oral cancer biomedical imaging Inception model hybrid deep learning COVID-19 CT-scan 3D image segmentation 3D UNet 3D ResUNet 3D VGGUNet 3D DenseUNet ultrasonic imaging kidney object detection vision loss diabetic retinopathy image enhancement APTOS stand-alone artificial intelligence radiology benchmarking population screening thema EDItEUR::M Medicine and Nursing In recent years, AI/ML tools have become more prevalent in the fields of medical imaging and imaging informatics, where systems are already outperforming physicians in a range of domains, such as in the classification of retinal fundus images in ophthalmology, chest X-rays in radiology, and skin cancer detection in dermatology, among many others. It has recently emerged as one of the fastest growing research areas given the evolution of techniques in radiology, molecular imaging, anatomical imaging, and functional imaging for detection, segmentation, diagnosis, annotation, summarization, and prediction. The ongoing innovations in this exciting and promising field play a powerful role in influencing the lives of millions through health, safety, education, and other opportunities intended to be shared across all segments of society. To achieve further progress, this Special Issue (SI) invited both research and review-type manuscripts to showcase ongoing research progress and development based on applications of AI/ML (especially DL techniques) in medical imaging to influence human health and healthcare systems in the diagnostic decision-making process. The SI published fourteen articles after a rigorous peer-review process across the spectrum of medical imaging modalities and the diversity of specialties depending on imaging techniques from radiology, dermatology, pathology, colonoscopy, endoscopy, etc. 2023-08-08T15:12:39Z 2023-08-08T15:12:39Z 2023 book ONIX_20230808_9783036581286_25 9783036581286 9783036581293 https://directory.doabooks.org/handle/20.500.12854/112457 eng image/jpeg Attribution 4.0 International https://mdpi.com/books/pdfview/book/7570 https://mdpi.com/books/pdfview/book/7570 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-8129-3 10.3390/books978-3-0365-8129-3 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036581286 9783036581293 238 Basel open access |
| spellingShingle | machine learning feature selection osteoporosis postmenopausal women pre-screening risk assessment colorectal cancer colon polyp image features convolutional neural network artificial intelligence radiomics pancreatic imaging MRI CT PET acute lymphoblastic leukemia (ALL) blood smear convolutional neural networks deep learning white blood cells dysarthria gated recurrent units ordinal classification multi-instance learning weak supervision breast cancer key instance uncertainty select mammography deep neural network classification HAM10000 skin lesion ESRGAN medical imaging healthcare decision making cervical cancer ensemble learning Internet of Medical Things oral cancer biomedical imaging Inception model hybrid deep learning COVID-19 CT-scan 3D image segmentation 3D UNet 3D ResUNet 3D VGGUNet 3D DenseUNet ultrasonic imaging kidney object detection vision loss diabetic retinopathy image enhancement APTOS stand-alone artificial intelligence radiology benchmarking population screening thema EDItEUR::M Medicine and Nursing Artificial Intelligence (AI) and Machine Learning (ML) in Medical Imaging Informatics towards Diagnostic Decision Making |
| title | Artificial Intelligence (AI) and Machine Learning (ML) in Medical Imaging Informatics towards Diagnostic Decision Making |
| title_full | Artificial Intelligence (AI) and Machine Learning (ML) in Medical Imaging Informatics towards Diagnostic Decision Making |
| title_fullStr | Artificial Intelligence (AI) and Machine Learning (ML) in Medical Imaging Informatics towards Diagnostic Decision Making |
| title_full_unstemmed | Artificial Intelligence (AI) and Machine Learning (ML) in Medical Imaging Informatics towards Diagnostic Decision Making |
| title_short | Artificial Intelligence (AI) and Machine Learning (ML) in Medical Imaging Informatics towards Diagnostic Decision Making |
| title_sort | artificial intelligence ai and machine learning ml in medical imaging informatics towards diagnostic decision making |
| topic | machine learning feature selection osteoporosis postmenopausal women pre-screening risk assessment colorectal cancer colon polyp image features convolutional neural network artificial intelligence radiomics pancreatic imaging MRI CT PET acute lymphoblastic leukemia (ALL) blood smear convolutional neural networks deep learning white blood cells dysarthria gated recurrent units ordinal classification multi-instance learning weak supervision breast cancer key instance uncertainty select mammography deep neural network classification HAM10000 skin lesion ESRGAN medical imaging healthcare decision making cervical cancer ensemble learning Internet of Medical Things oral cancer biomedical imaging Inception model hybrid deep learning COVID-19 CT-scan 3D image segmentation 3D UNet 3D ResUNet 3D VGGUNet 3D DenseUNet ultrasonic imaging kidney object detection vision loss diabetic retinopathy image enhancement APTOS stand-alone artificial intelligence radiology benchmarking population screening thema EDItEUR::M Medicine and Nursing |
| topic_facet | machine learning feature selection osteoporosis postmenopausal women pre-screening risk assessment colorectal cancer colon polyp image features convolutional neural network artificial intelligence radiomics pancreatic imaging MRI CT PET acute lymphoblastic leukemia (ALL) blood smear convolutional neural networks deep learning white blood cells dysarthria gated recurrent units ordinal classification multi-instance learning weak supervision breast cancer key instance uncertainty select mammography deep neural network classification HAM10000 skin lesion ESRGAN medical imaging healthcare decision making cervical cancer ensemble learning Internet of Medical Things oral cancer biomedical imaging Inception model hybrid deep learning COVID-19 CT-scan 3D image segmentation 3D UNet 3D ResUNet 3D VGGUNet 3D DenseUNet ultrasonic imaging kidney object detection vision loss diabetic retinopathy image enhancement APTOS stand-alone artificial intelligence radiology benchmarking population screening thema EDItEUR::M Medicine and Nursing |
| url | ONIX_20230808_9783036581286_25 |