AIM: To compare the image quality obtained using two accelerated high-resolution 3D fluid-attenuated inversion recovery (FLAIR) techniques for the brain-deep learning-reconstruction SPACE (DL-SPACE) and Wave-CAIPI FLAIR.
OBJECTIVES: To evaluate the value of deep-learning-based intratumoral and peritumoral features for differentiating ocular adnexal lymphoma (OAL) and idiopathic orbital inflammation (IOI).
Medical image analysis has significantly benefited from advancements in deep learning, particularly in the application of Generative Adversarial Networks (GANs) for generating realistic and diverse images that can augment training datasets. The commo...
RATIONALE AND OBJECTIVES: Magnetic resonance imaging (MRI) is a vital tool for diagnosing neurological disorders, frequently utilising gadolinium-based contrast agents (GBCAs) to enhance resolution and specificity. However, GBCAs present certain risk...
BACKGROUND: Alzheimer's Disease is a neurodegenerative condition leading to irreversible and progressive brain damage, with possible features such as structural atrophy. Effective precision diagnosis is crucial for slowing disease progression and red...
Recognition and segmentation of brain tumours (BT) using MR images are valuable and tedious processes in the healthcare industry. Earlier diagnosis and localization of BT provide timely options to select effective treatment plans for the doctors and ...
Deep learning (DL) techniques represent a rapidly advancing field within artificial intelligence, gaining significant prominence in the detection and analysis of various medical conditions through the analysis of medical data. This study presents a s...
International journal of gynecological cancer : official journal of the International Gynecological Cancer Society
Dec 17, 2024
OBJECTIVE: Evaluation of prognostic factors is crucial in patients with endometrial cancer for optimal treatment planning and prognosis assessment. This study proposes a deep learning pipeline for tumor and uterus segmentation from magnetic resonance...