AIMC Topic: Image Interpretation, Computer-Assisted
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Lymphoma triage from H&E using AI for improved clinical management.
Journal of clinical pathology
Dec 18, 2024
AIMS: In routine diagnosis of lymphoma, initial non-specialist triage is carried out when the sample is biopsied to determine if referral to specialised haematopathology services is needed. This places a heavy burden on pathology services, causes del...
Incorporating Radiologist Knowledge Into MRI Quality Metrics for Machine Learning Using Rank-Based Ratings.
Journal of magnetic resonance imaging : JMRI
Dec 17, 2024
BACKGROUND: Deep learning (DL) often requires an image quality metric; however, widely used metrics are not designed for medical images.
Attention-based Fusion Network for Breast Cancer Segmentation and Classification Using Multi-modal Ultrasound Images.
Ultrasound in medicine & biology
Dec 17, 2024
OBJECTIVE: Breast cancer is one of the most commonly occurring cancers in women. Thus, early detection and treatment of cancer lead to a better outcome for the patient. Ultrasound (US) imaging plays a crucial role in the early detection of breast can...
Deep Learning techniques to detect and analysis of multiple sclerosis through MRI: A systematic literature review.
Computers in biology and medicine
Dec 17, 2024
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...
Standardized evaluation of diabetic retinopathy using artificial intelligence and its association with metabolic dysfunction-associated steatotic liver disease in Japan: A cross-sectional study.
PloS one
Dec 17, 2024
Metabolic dysfunction-associated steatotic liver disease (MASLD) is common in patients with obesity and diabetes and can lead to serious complications. This study aimed to evaluate fundus photographs using artificial intelligence to explore the relat...
Male
Image Interpretation, Computer-Assisted
Female
Humans
Middle Aged
Glycated Hemoglobin
Diabetic Retinopathy
Risk Factors
Severity of Illness Index
Artificial Intelligence
Photography
Fundus Oculi
Sensitivity and Specificity
Protective Factors
Liver Cirrhosis
Non-alcoholic Fatty Liver Disease
Software
Cholinesterases
Cross-Sectional Studies
Japan
Interpretable Deep-learning Model Based on Superb Microvascular Imaging for Noninvasive Diagnosis of Interstitial Fibrosis in Chronic Kidney Disease.
Academic radiology
Dec 16, 2024
RATIONALE AND OBJECTIVES: To develop an interpretable deep learning (XDL) model based on superb microvascular imaging (SMI) for the noninvasive diagnosis of the degree of interstitial fibrosis (IF) in chronic kidney disease (CKD).
A Fusion Model of MRI Deep Transfer Learning and Radiomics for Discriminating between Pilocytic Astrocytoma and Adamantinomatous Craniopharyngioma.
Academic radiology
Dec 16, 2024
RATIONALE AND OBJECTIVES: This study aimed to develop and validate a fusion model combining MRI deep transfer learning (DTL) and radiomics for discriminating between pilocytic astrocytoma (PA) and adamantinomatous craniopharyngioma (ACP) in the sella...
Noncontrast MRI-based machine learning and radiomics signature can predict the severity of primary lower limb lymphedema.
Journal of vascular surgery. Venous and lymphatic disorders
Dec 16, 2024
OBJECTIVE: According to International Lymphology Society guidelines, the severity of lymphedema is determined by the difference in volume between the affected limb and the healthy side divided by the volume of the healthy side. However, this method o...
L-MAE: Longitudinal masked auto-encoder with time and severity-aware encoding for diabetic retinopathy progression prediction.
Computers in biology and medicine
Dec 16, 2024
Pre-training strategies based on self-supervised learning (SSL) have demonstrated success as pretext tasks for downstream tasks in computer vision. However, while SSL methods are often domain-agnostic, their direct application to medical imaging is c...
Predicting molecular subtypes of breast cancer based on multi-parametric MRI dataset using deep learning method.
Magnetic resonance imaging
Dec 14, 2024
PURPOSE: To develop a multi-parametric MRI model for the prediction of molecular subtypes of breast cancer using five types of breast cancer preoperative MRI images.
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