Artificial intelligence-based radiomics in bone tumors: Technical advances and clinical application
Y Meng, Y Yang, M Hu, Z Zhang, X Zhou - Seminars in Cancer Biology, 2023 - Elsevier
Radiomics is the extraction of predefined mathematic features from medical images for
predicting variables of clinical interest. Recent research has demonstrated that radiomics …
predicting variables of clinical interest. Recent research has demonstrated that radiomics …
[HTML][HTML] Image-based generative artificial intelligence in radiology: comprehensive updates
Generative artificial intelligence (AI) has been applied to images for image quality
enhancement, domain transfer, and augmentation of training data for AI modeling in various …
enhancement, domain transfer, and augmentation of training data for AI modeling in various …
In vivo repeatability and multiscanner reproducibility of MRI radiomics features in patients with monoclonal plasma cell disorders: a prospective bi-institutional study
Objectives Despite the extensive number of publications in the field of radiomics, radiomics
algorithms barely enter large-scale clinical application. Supposedly, the low external …
algorithms barely enter large-scale clinical application. Supposedly, the low external …
Influence of image processing on radiomic features from magnetic resonance imaging
BD Wichtmann, FN Harder, K Weiss… - Investigative …, 2023 - journals.lww.com
Objective Before implementing radiomics in routine clinical practice, comprehensive
knowledge about the repeatability and reproducibility of radiomic features is required. The …
knowledge about the repeatability and reproducibility of radiomic features is required. The …
Combining deep learning and radiomics for automated, objective, comprehensive bone marrow characterization from whole-body MRI: a multicentric feasibility study
Objectives Disseminated bone marrow (BM) involvement is frequent in multiple myeloma
(MM). Whole-body magnetic resonance imaging (wb-MRI) enables to evaluate the whole …
(MM). Whole-body magnetic resonance imaging (wb-MRI) enables to evaluate the whole …
[HTML][HTML] Generative adversarial network-based image conversion among different computed tomography protocols and vendors: effects on accuracy and variability in …
Objective To assess whether computed tomography (CT) conversion across different scan
parameters and manufacturers using a routable generative adversarial network (RouteGAN) …
parameters and manufacturers using a routable generative adversarial network (RouteGAN) …
Deep learning–assisted diagnosis of benign and malignant parotid tumors based on contrast-enhanced CT: a multicenter study
Q Yu, Y Ning, A Wang, S Li, J Gu, Q Li, X Chen, F Lv… - European …, 2023 - Springer
Objectives To develop deep learning–assisted diagnosis models based on CT images to
facilitate radiologists in differentiating benign and malignant parotid tumors. Methods Data …
facilitate radiologists in differentiating benign and malignant parotid tumors. Methods Data …
Deep learning reconstruction improves radiomics feature stability and discriminative power in abdominal CT imaging: a phantom study
F Michallek, U Genske, SM Niehues, B Hamm… - European …, 2022 - Springer
Objectives To compare image quality of deep learning reconstruction (AiCE) for radiomics
feature extraction with filtered back projection (FBP), hybrid iterative reconstruction (AIDR …
feature extraction with filtered back projection (FBP), hybrid iterative reconstruction (AIDR …
[HTML][HTML] Improvement in Image Quality of Low-Dose CT of Canines with Generative Adversarial Network of Anti-Aliasing Generator and Multi-Scale Discriminator
Computed tomography (CT) imaging is vital for diagnosing and monitoring diseases in both
humans and animals, yet radiation exposure remains a significant concern, especially in …
humans and animals, yet radiation exposure remains a significant concern, especially in …
Differentiation of benign and malignant vertebral fractures using a convolutional neural network to extract CT-based texture features
SS Goller, SC Foreman, JF Rischewski… - European spine …, 2023 - Springer
Purpose To assess the diagnostic performance of three-dimensional (3D) CT-based texture
features (TFs) using a convolutional neural network (CNN)-based framework to differentiate …
features (TFs) using a convolutional neural network (CNN)-based framework to differentiate …