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Artificial Intelligence (AI) and Machine Learning (ML) in Medical Imaging Informatics towards Diagnostic Decision Making
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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.
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Keywords
- 3D DenseUNet
- 3D image segmentation
- 3D ResUNet
- 3D UNet
- 3D VGGUNet
- acute lymphoblastic leukemia (ALL)
- APTOS
- artificial intelligence
- Benchmarking
- biomedical imaging
- blood smear
- Breast cancer
- cervical cancer
- Classification
- colon polyp
- colorectal cancer
- convolutional neural network
- convolutional neural networks
- COVID-19 CT-scan
- CT
- Decision making
- deep learning
- deep neural network
- diabetic retinopathy
- dysarthria
- ensemble learning
- ESRGAN
- feature selection
- gated recurrent units
- HAM10000
- Healthcare
- hybrid deep learning
- image enhancement
- image features
- inception model
- Internet of Medical Things
- key instance
- Kidney
- Machine learning
- mammography
- Medical imaging
- medicine
- MRI
- multi-instance learning
- object detection
- oral cancer
- ordinal classification
- osteoporosis
- pancreatic imaging
- pet
- population screening
- postmenopausal women
- pre-screening
- Radiology
- radiomics
- Risk assessment
- skin lesion
- stand-alone artificial intelligence
- Ultrasonic imaging
- uncertainty select
- vision loss
- weak supervision
- white blood cells
Links
DOI: 10.3390/books978-3-0365-8129-3Editions