New Molecular and Functional Imaging Techniques

Gómez‐Vallejo, Vanessa; Jiménez-González, María; Llop, Jordi; Reese, Torsten

doi:10.1007/978-3-642-40412-2_22

Cited by 2 publications

(3 citation statements)

References 132 publications

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“…It contributes to a deeper understanding of physiological processes, metabolic functions, and the mechanisms underlying various abdominal-related conditions. Func-tional imaging techniques, such as dynamic contrast-enhanced MRI and nuclear medicine scans, can reveal how different abdominal organs respond to specific stimuli or diseases [178]. Moreover, abdominal imaging is indispensable in developing and evaluating novel medical interventions and therapies.…”

Section: Abdominal Imagingmentioning

confidence: 99%

Machine Learning Empowering Personalized Medicine: A Comprehensive Review of Medical Image Analysis Methods

Galić,

Habijan,

Leventić

et al. 2023

Electronics

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Artificial intelligence (AI) advancements, especially deep learning, have significantly improved medical image processing and analysis in various tasks such as disease detection, classification, and anatomical structure segmentation. This work overviews fundamental concepts, state-of-the-art models, and publicly available datasets in the field of medical imaging. First, we introduce the types of learning problems commonly employed in medical image processing and then proceed to present an overview of commonly used deep learning methods, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs), with a focus on the image analysis task they are solving, including image classification, object detection/localization, segmentation, generation, and registration. Further, we highlight studies conducted in various application areas, encompassing neurology, brain imaging, retinal analysis, pulmonary imaging, digital pathology, breast imaging, cardiac imaging, bone analysis, abdominal imaging, and musculoskeletal imaging. The strengths and limitations of each method are carefully examined, and the paper identifies pertinent challenges that still require attention, such as the limited availability of annotated data, variability in medical images, and the interpretability issues. Finally, we discuss future research directions with a particular focus on developing explainable deep learning methods and integrating multi-modal data.

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Section: Abdominal Imagingmentioning

confidence: 99%

Machine Learning Empowering Personalized Medicine: A Comprehensive Review of Medical Image Analysis Methods

Galić,

Habijan,

Leventić

et al. 2023

Electronics

View full text Add to dashboard Cite

show abstract

“…Micelles are nano-sized colloidal carriers with a hydrophobic core and hydrophilic shell (Figure 1). While the core acts as a reservoir for hydrophobic drugs, the shell of micelles provides hydrophilic properties for the system [2,3].…”

Section: Micellesmentioning

confidence: 99%

“…They have also other advantageous including improvement of the chemical stability of drugs, and easy scale-up procedure for industrial production. Micelles have therefore been widely investigated for use in the nasal, ocular, and skin delivery of drugs to overcome the natural transport barrier of biological membranes [1][2][3].…”

Section: Introductionmentioning

confidence: 99%