Artificial Intelligence in Medical Image Processing and Segmentation
This reprint showcases a selection of bleeding-edge articles about medical image processing and segmentation workflows based on artificial intelligence algorithms. The proposed papers are applied to multiple and different anatomical districts and clinical scenarios.
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Format: | Electronic Book Chapter |
Language: | English |
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MDPI - Multidisciplinary Digital Publishing Institute
2023
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Online Access: | DOAB: download the publication DOAB: description of the publication |
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100 | 1 | |a Zaffino, Paolo |4 edt | |
700 | 1 | |a Spadea, Maria Francesca |4 edt | |
700 | 1 | |a Zaffino, Paolo |4 oth | |
700 | 1 | |a Spadea, Maria Francesca |4 oth | |
245 | 1 | 0 | |a Artificial Intelligence in Medical Image Processing and Segmentation |
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338 | |a online resource |b cr |2 rdacarrier | ||
506 | 0 | |a Open Access |2 star |f Unrestricted online access | |
520 | |a This reprint showcases a selection of bleeding-edge articles about medical image processing and segmentation workflows based on artificial intelligence algorithms. The proposed papers are applied to multiple and different anatomical districts and clinical scenarios. | ||
540 | |a Creative Commons |f https://creativecommons.org/licenses/by/4.0/ |2 cc |4 https://creativecommons.org/licenses/by/4.0/ | ||
546 | |a English | ||
650 | 7 | |a Technology: general issues |2 bicssc | |
650 | 7 | |a History of engineering & technology |2 bicssc | |
653 | |a fundus image | ||
653 | |a image registration | ||
653 | |a deep learning | ||
653 | |a computer vision applications | ||
653 | |a nuclei segmentation | ||
653 | |a histopathology | ||
653 | |a Grad-CAM | ||
653 | |a semantic segmentation | ||
653 | |a instance segmentation | ||
653 | |a nuclei detection | ||
653 | |a pap smear | ||
653 | |a cervical net | ||
653 | |a shuffle net | ||
653 | |a canonical correlation analysis (CCA) | ||
653 | |a support vector machine (SVM) | ||
653 | |a random forest (RF) | ||
653 | |a k-nearest neighbour (KNN) | ||
653 | |a artificial neural network (ANN) | ||
653 | |a dual-energy CT | ||
653 | |a two-step method | ||
653 | |a limited-angular range | ||
653 | |a directional total variation | ||
653 | |a PA | ||
653 | |a CNN | ||
653 | |a tooth disease recognition | ||
653 | |a image segmentation | ||
653 | |a image preprocessing | ||
653 | |a breast cancer | ||
653 | |a mitotic nuclei classification | ||
653 | |a histopathology images | ||
653 | |a artificial hummingbird algorithm | ||
653 | |a medical imaging | ||
653 | |a cervical cancer | ||
653 | |a feature fusion | ||
653 | |a feature selection | ||
653 | |a deep learning structures | ||
653 | |a support vector machine | ||
653 | |a disease discrimination accuracy | ||
653 | |a performance comparisons | ||
653 | |a 2D/3D registration | ||
653 | |a orthogonal X-ray | ||
653 | |a breast density | ||
653 | |a CAD | ||
653 | |a image enhancement | ||
653 | |a textural | ||
653 | |a auto-segmentation | ||
653 | |a neuroimaging | ||
653 | |a magnetic resonance imaging | ||
653 | |a ovarian tumor | ||
653 | |a 2D ultrasound image | ||
653 | |a image inpainting | ||
653 | |a lesion segmentation | ||
653 | |a attention mechanism | ||
653 | |a GAN | ||
653 | |a medical image analysis | ||
653 | |a synthetic CT | ||
653 | |a MRI guidance | ||
653 | |a MRI-only | ||
653 | |a image-guided radiotherapy | ||
653 | |a carbon ion radiotherapy | ||
653 | |a particle therapy | ||
653 | |a rare tumor | ||
653 | |a PCNSL | ||
653 | |a radiomics | ||
653 | |a image normalization | ||
653 | |a MRI | ||
653 | |a prostate cancer | ||
653 | |a prostate segmentation | ||
653 | |a U-Net | ||
653 | |a mp-MRI | ||
653 | |a loss function | ||
653 | |a automatic volume measurement | ||
653 | |a ultrasound bladder scanner | ||
653 | |a edge computing | ||
653 | |a urinary disease | ||
653 | |a artificial intelligence | ||
653 | |a mandible | ||
653 | |a segmentation | ||
653 | |a 3D virtual reconstruction | ||
653 | |a CBCT | ||
653 | |a CT | ||
653 | |a Convolutional Neural Networks | ||
653 | |a comparison | ||
653 | |a in-house | ||
653 | |a software | ||
653 | |a patch size | ||
653 | |a Cranio-Maxillofacial surgery | ||
653 | |a DICOM | ||
653 | |a osteoarthritis | ||
653 | |a histopathological | ||
653 | |a hematoxylin eosin | ||
653 | |a safranin O fast green | ||
653 | |a DarkNet-19 | ||
653 | |a MobileNet | ||
653 | |a NasNet | ||
653 | |a ResNet-101 | ||
653 | |a ShuffleNet | ||
653 | |a PCA | ||
653 | |a ALO | ||
653 | |a ensemble learning | ||
653 | |a OCT | ||
653 | |a pyramidal network | ||
653 | |a scale-adaptive | ||
653 | |a teeth segmentation | ||
653 | |a panoramic radiographs | ||
653 | |a mask-transformer-based networks | ||
653 | |a panoptic segmentation | ||
653 | |a tuberous sclerosis complex | ||
653 | |a children | ||
653 | |a convolutional neural network | ||
653 | |a multi-contrast MRI | ||
653 | |a rare neurodevelopmental disorder | ||
856 | 4 | 0 | |a www.oapen.org |u https://mdpi.com/books/pdfview/book/7837 |7 0 |z DOAB: download the publication |
856 | 4 | 0 | |a www.oapen.org |u https://directory.doabooks.org/handle/20.500.12854/113993 |7 0 |z DOAB: description of the publication |