International Journal of Neural Systems Authors Enol García González, Mădălina Dicu, José R. Villar, Camelia Chira Abstract The automatic detection of anomalies in medical images is a significant challenge in the assisted diagnosis of neurodegenerative diseases such as Alzheimer’s. This paper presents an anomaly detection model based on Transformers for the analysis of brain…
Introduction Magnetic resonance imaging (MRI) is conventionally used for the detection and diagnosis of multiple sclerosis (MS), often complemented by lumbar puncture—a highly invasive method—to validate the diagnosis. Additionally, MRI is periodically repeated to monitor disease progression and treatment efficacy. Recent research has focused on the application of artificial intelligence (AI) and radiomics in…
Background: Prostate cancer (PCa) is the most frequent neoplasia in the male population. According to the International Society of Urological Pathology (ISUP), PCa can be divided into two major groups, based on their prognosis and treatment options. Multiparametric magnetic resonance imaging (mpMRI) holds a central role in PCa assessment; however, it does not have…
The integration of Artificial Intelligence (AI), particularly deep learning models like VGG16 and ResNet50, in the analysis of functional magnetic resonance imaging (fMRI) data has significantly advanced our understanding of brain functionality and the diagnosis of neurological disorders. This paper explores the application of Convolutional Neural Networks (CNNs) to enhance the accuracy and efficiency…
Brain disorders are increasingly prevalent today, making accurate brain segmentation essential for effective treatment andrecovery. This paperintroducesanenhancedunsupervisedgraph-basedbrainsegmentationmethod that employs an ellipsoid to select the nodes forming the graph. The method was rigorously evaluated on T1 and T2 modalities using four diverse datasets: the complete NFBS dataset, 48 MRIs from the IXI dataset, 16 images…
INTRODUCTION Prostate magnetic resonance imaging (MRI) has been recently integrated into the pathway of diagnosis of prostate cancer (PCa). However, the lack of an optimal contrast-to-noise ratio hinders automatic recognition of suspicious lesions, thus developing a solution for proper delimitation of the tumour and its separation from the healthy parenchyma, which is of primordial…
Introduction Bladder magnetic resonance imaging (MRI) has been recently integrated in the diagnosis pathway of bladder cancer. However, automatic recognition of suspicious lesions is still challenging. Thus, development of a solution for proper delimitation of the tumor and its separation from the healthy tissue is of primordial importance. As a solution to this unmet…
Background: With the recent introduction of vesical imaging reporting and data system (VI-RADS), magnetic resonance imaging (MRI) has become the main imaging method used for the preoperative local staging of bladder cancer (BCa). However, the VI-RADS score is subject to interobserver variability and cannot provide information about tumor cellularity. These limitations may be overcome…
Introduction: Multiparametric magnetic resonance imaging (mpMRI) is the main imagistic tool employed to assess patients suspected of harboring prostate cancer (PCa), setting the indication for targeted prostate biopsy. However, both mpMRI and targeted prostate biopsy are operator dependent. The past decade has been marked by the emerging domain of radiomics and artificial intelligence (AI),…
S. Manole, L. Popa, C. Ciortea, C. Mihaela, C. Szabo, S. Sfrângeu, L. Dioșan, A. Marinescu, Z. Bálint, Assessment of the heart with atrial fibrillation using a Magnetic Resonance Imaging (MRI) method, Romanian Congress of Radiology and Medical Imaging, e-poster, Bucharest, October 6-8, 2017, http://srim2017.medical-congresses.ro/
