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Relay learning: a physically secure framework for clinical multi-site deep learning
Big data serves as the cornerstone for constructing real-world deep learning systems across various domains. In medicine and healthcare, a single...
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Efficient deep learning-based automated diagnosis from echocardiography with contrastive self-supervised learning
BackgroundAdvances in self-supervised learning (SSL) have enabled state-of-the-art automated medical image diagnosis from small, labeled datasets....
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Integrated approach of federated learning with transfer learning for classification and diagnosis of brain tumor
Brain tumor classification using MRI images is a crucial yet challenging task in medical imaging. Accurate diagnosis is vital for effective treatment...
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Contrastive Learning vs. Self-Learning vs. Deformable Data Augmentation in Semantic Segmentation of Medical Images
To develop a robust segmentation model, encoding the underlying features/structures of the input data is essential to discriminate the target...
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Develo** a novel causal inference algorithm for personalized biomedical causal graph learning using meta machine learning
BackgroundModeling causality through graphs, referred to as causal graph learning, offers an appropriate description of the dynamics of causality....
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A Comparative Study of Performance Between Federated Learning and Centralized Learning Using Pathological Image of Endometrial Cancer
Federated learning, an innovative artificial intelligence training method, offers a secure solution for institutions to collaboratively develop...
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Self Directed Learning: A More Impactful Tool for Learning Tracheostomy by Medical Undergraduate?!
An educational project in medical undergraduate otorhinolaryngology teaching-learningmethodology was designed with the aimof objectively studying the...
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College students’ learning stress, psychological resilience and learning burnout: status quo and co** strategies
BackgroundThe relationships of college students’ learning stress, psychological resilience and learning burnout remain unclear. We aimed to...
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Machine learning and deep learning for classifying the justification of brain CT referrals
ObjectivesTo train the machine and deep learning models to automate the justification analysis of radiology referrals in accordance with iGuide...
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Integrated machine learning and deep learning for predicting diabetic nephropathy model construction, validation, and interpretability
ObjectiveTo construct a risk prediction model for assisted diagnosis of Diabetic Nephropathy (DN) using machine learning algorithms, and to validate...
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Deep learning in rheumatological image interpretation
Artificial intelligence techniques, specifically deep learning, have already affected daily life in a wide range of areas. Likewise, initial...
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Learning Urogenital Diseases in Oddity (LUDO)—a gamification-based innovation for learning urogenital diseases in emergency medicine
Urogenital emergencies demand immediate attention within the field of emergency medicine, encompassing a range of critical conditions from ectopic...
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Effectiveness and learning experience from undergraduate nursing students in surgical nursing skills course: a quasi- experimental study about blended learning
BackgroundBlended learning is increasingly being adopted, and yet a gap remains in the related literature pertaining to its skill performance,...
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Spatial and geometric learning for classification of breast tumors from multi-center ultrasound images: a hybrid learning approach
BackgroundBreast cancer is the most common cancer among women, and ultrasound is a usual tool for early screening. Nowadays, deep learning technique...
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Machine learning, deep learning and hernia surgery. Are we pushing the limits of abdominal core health? A qualitative systematic review
IntroductionThis systematic review aims to evaluate the use of machine learning and artificial intelligence in hernia surgery.
MethodsThe PRISMA...
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An active learning approach to train a deep learning algorithm for tumor segmentation from brain MR images
PurposeThis study focuses on assessing the performance of active learning techniques to train a brain MRI glioma segmentation model.
Methods ...
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Radiomics-based machine learning and deep learning to predict serosal involvement in gallbladder cancer
ObjectiveOur study aimed to determine whether radiomics models based on contrast-enhanced computed tomography (CECT) have considerable ability to...
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Deep Learning Glioma Grading with the Tumor Microenvironment Analysis Protocol for Comprehensive Learning, Discovering, and Quantifying Microenvironmental Features
Gliomas are primary brain tumors that arise from neural stem cells, or glial precursors. Diagnosis of glioma is based on histological evaluation of...
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Learning does not just happen: establishing learning principles for tools to translate resilience into practice, based on a participatory approach
BackgroundTheories of learning are of clear importance to resilience in healthcare since the ability to successfully adapt and improve patient care...
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Decoding depression: a comprehensive multi-cohort exploration of blood DNA methylation using machine learning and deep learning approaches
The causes of depression are complex, and the current diagnosis methods rely solely on psychiatric evaluations with no incorporation of laboratory...
