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A reliable diabetic retinopathy grading via transfer learning and ensemble learning with quadratic weighted kappa metric
The most common eye infection in people with diabetes is diabetic retinopathy (DR). It might cause blurred vision or even total blindness. Therefore,...
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Patient Identification Based on Deep Metric Learning for Preventing Human Errors in Follow-up X-Ray Examinations
Biological fingerprints extracted from clinical images can be used for patient identity verification to determine misfiled clinical images in picture...
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Automated classification of clinical trial eligibility criteria text based on ensemble learning and metric learning
BackgroundEligibility criteria are the primary strategy for screening the target participants of a clinical trial. Automated classification of...
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Patient Re-Identification Based on Deep Metric Learning in Trunk Computed Tomography Images Acquired from Devices from Different Vendors
During radiologic interpretation, radiologists read patient identifiers from the metadata of medical images to recognize the patient being examined....
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Comparison of machine learning–based algorithms using corneal asymmetry vs. single-metric parameters for keratoconus detection
PurposeTo evaluate the diagnostic performance of three different parameter sets relevant to corneal asymmetry in comparison to conventional...
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Automated classification of polyps using deep learning architectures and few-shot learning
BackgroundColorectal cancer is a leading cause of cancer-related deaths worldwide. The best method to prevent CRC is a colonoscopy. However, not all...
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Learning to deep learning: statistics and a paradigm test in selecting a UNet architecture to enhance MRI
ObjectiveThis study aims to assess the statistical significance of training parameters in 240 dense UNets (DUNets) used for enhancing low...
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Two-Stage CNN Whole Heart Segmentation Combining Image Enhanced Attention Mechanism and Metric Classification
Accurate segmentation of multiple tissues and organs in cardiac medical imaging is of great value in computer-aided cardiovascular diagnosis....
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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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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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Active control time: an objective performance metric for trainee participation in robotic surgery
Trainee participation and progression in robotic general surgery remain poorly defined. Computer-assisted technology offers the potential to provide...
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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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Making machine learning matter to clinicians: model actionability in medical decision-making
Machine learning (ML) has the potential to transform patient care and outcomes. However, there are important differences between measuring the...
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Sources of performance variability in deep learning-based polyp detection
PurposeValidation metrics are a key prerequisite for the reliable tracking of scientific progress and for deciding on the potential clinical...
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Histopathologic brain age estimation via multiple instance learning
Understanding age acceleration, the discordance between biological and chronological age, in the brain can reveal mechanistic insights into normal...
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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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Ethnic disparity in diagnosing asymptomatic bacterial vaginosis using machine learning
While machine learning (ML) has shown great promise in medical diagnostics, a major challenge is that ML models do not always perform equally well...
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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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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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Machine learning and deep learning techniques for breast cancer diagnosis and classification: a comprehensive review of medical imaging studies
BackgroundBreast cancer is a major public health concern, and early diagnosis and classification are critical for effective treatment. Machine...
