AIMC Topic:
Magnetic Resonance Imaging
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The Contribution of Explainable Machine Learning Algorithms Using ROI-based Brain Surface Morphology Parameters in Distinguishing Early-onset Schizophrenia From Bipolar Disorder.
Academic radiology
May 3, 2024
RATIONALE AND OBJECTIVES: To differentiate early-onset schizophrenia (EOS) from early-onset bipolar disorder (EBD) using surface-based morphometry measurements and brain volumes using machine learning (ML) algorithms.
Letter to the editor: Magnetic resonance imaging-based radiomics and deep learning models for predicting lymph node metastasis of squamous cell carcinoma of the tongue.
Oral surgery, oral medicine, oral pathology and oral radiology
May 3, 2024
Radiomics model and deep learning model based on T1WI image for acute lymphoblastic leukemia identification.
Clinical radiology
May 3, 2024
AIM: This study aimed to develop highly precise radiomics and deep learning models to accurately detect acute lymphoblastic leukemia (ALL) using a T1WI image.
PSMA-positive prostatic volume prediction with deep learning based on T2-weighted MRI.
La Radiologia medica
May 3, 2024
PURPOSE: High PSMA expression might be correlated with structural characteristics such as growth patterns on histopathology, not recognized by the human eye on MRI images. Deep structural image analysis might be able to detect such differences and th...
Artificial intelligence for volumetric measurement of cerebral white matter hyperintensities on thick-slice fluid-attenuated inversion recovery (FLAIR) magnetic resonance images from multiple centers.
Scientific reports
May 2, 2024
We aimed to develop a new artificial intelligence software that can automatically extract and measure the volume of white matter hyperintensities (WMHs) in head magnetic resonance imaging (MRI) using only thick-slice fluid-attenuated inversion recove...
Encoding Enhanced Complex CNN for Accurate and Highly Accelerated MRI.
IEEE transactions on medical imaging
May 2, 2024
Magnetic resonance imaging (MRI) using hyperpolarized noble gases provides a way to visualize the structure and function of human lung, but the long imaging time limits its broad research and clinical applications. Deep learning has demonstrated grea...
Bilateral Supervision Network for Semi-Supervised Medical Image Segmentation.
IEEE transactions on medical imaging
May 2, 2024
Massive high-quality annotated data is required by fully-supervised learning, which is difficult to obtain for image segmentation since the pixel-level annotation is expensive, especially for medical image segmentation tasks that need domain knowledg...
Attentional adversarial training for few-shot medical image segmentation without annotations.
PloS one
May 2, 2024
Medical image segmentation is a critical application that plays a significant role in clinical research. Despite the fact that many deep neural networks have achieved quite high accuracy in the field of medical image segmentation, there is still a sc...
Deep Learning Synthesis of White-Blood From Dark-Blood Late Gadolinium Enhancement Cardiac Magnetic Resonance.
Investigative radiology
May 1, 2024
OBJECTIVES: Dark-blood late gadolinium enhancement (DB-LGE) cardiac magnetic resonance has been proposed as an alternative to standard white-blood LGE (WB-LGE) imaging protocols to enhance scar-to-blood contrast without compromising scar-to-myocardiu...
Deep learning in magnetic resonance enterography for Crohn's disease assessment: a systematic review.
Abdominal radiology (New York)
May 1, 2024
Crohn's disease (CD) poses significant morbidity, underscoring the need for effective, non-invasive inflammatory assessment using magnetic resonance enterography (MRE). This literature review evaluates recent publications on the role of deep learning...
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