Artificial intelligence applications in medical imaging: A review of the medical physics research in Italy
Purpose To perform a systematic review on the research on the application of artificial
intelligence (AI) to imaging published in Italy and identify its fields of application, methods …
intelligence (AI) to imaging published in Italy and identify its fields of application, methods …
[HTML][HTML] Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation
Automatic segmentation methods are an important advancement in medical image analysis.
Machine learning techniques, and deep neural networks in particular, are the state-of-the-art …
Machine learning techniques, and deep neural networks in particular, are the state-of-the-art …
MADGAN: Unsupervised medical anomaly detection GAN using multiple adjacent brain MRI slice reconstruction
Background Unsupervised learning can discover various unseen abnormalities, relying on
large-scale unannotated medical images of healthy subjects. Towards this, unsupervised …
large-scale unannotated medical images of healthy subjects. Towards this, unsupervised …
A survey on nature-inspired medical image analysis: a step further in biomedical data integration
Natural phenomena and mechanisms have always intrigued humans, inspiring the design of
effective solutions for real-world problems. Indeed, fascinating processes occur in nature …
effective solutions for real-world problems. Indeed, fascinating processes occur in nature …
Fast level set method for glioma brain tumor segmentation based on Superpixel fuzzy clustering and lattice Boltzmann method
A Khosravanian, M Rahmanimanesh… - Computer Methods and …, 2021 - Elsevier
Abstract Background and Objective Brain tumor segmentation is a challenging issue due to
noise, artifact, and intensity non-uniformity in magnetic resonance images (MRI). Manual …
noise, artifact, and intensity non-uniformity in magnetic resonance images (MRI). Manual …
Pixel-wise regression using U-Net and its application on pansharpening
Convolutional neural networks are widely used for solving image recognition and other
classification problems in which the whole image is considered as a single object. In this …
classification problems in which the whole image is considered as a single object. In this …
MedGA: a novel evolutionary method for image enhancement in medical imaging systems
Medical imaging systems often require the application of image enhancement techniques to
help physicians in anomaly/abnormality detection and diagnosis, as well as to improve the …
help physicians in anomaly/abnormality detection and diagnosis, as well as to improve the …
[HTML][HTML] Auto-segmentation of head and neck tumors in positron emission tomography images using non-local means and morphological frameworks
S Heydarheydari, MJT Birgani… - Polish Journal of …, 2023 - ncbi.nlm.nih.gov
Purpose Accurately segmenting head and neck cancer (HNC) tumors in medical images is
crucial for effective treatment planning. However, current methods for HNC segmentation are …
crucial for effective treatment planning. However, current methods for HNC segmentation are …
Infinite brain MR images: PGGAN-based data augmentation for tumor detection
Due to the lack of available annotated medical images, accurate computer-assisted
diagnosis requires intensive data augmentation (DA) techniques, such as …
diagnosis requires intensive data augmentation (DA) techniques, such as …
Prediction of glioma grades using deep learning with wavelet radiomic features
G Çinarer, BG Emiroğlu, AH Yurttakal - Applied Sciences, 2020 - mdpi.com
Gliomas are the most common primary brain tumors. They are classified into 4 grades
(Grade I–II-III–IV) according to the guidelines of the World Health Organization (WHO). The …
(Grade I–II-III–IV) according to the guidelines of the World Health Organization (WHO). The …