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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Bibliographic Details
Other Authors: Zaffino, Paolo (Editor), Spadea, Maria Francesca (Editor)
Format: Electronic Book Chapter
Language:English
Published: MDPI - Multidisciplinary Digital Publishing Institute 2023
Subjects:
PA
CNN
CAD
GAN
MRI
CT
PCA
ALO
OCT
Online Access:DOAB: download the publication
DOAB: description of the publication
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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. 
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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 
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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