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Deep Transfer Learning for Ethnically Distinct Populations: Prediction of Refractive Error Using Optical Coherence Tomography
IntroductionThe mismatch between training and testing data distribution causes significant degradation in the deep learning model performance in...
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Deep transfer learning with fuzzy ensemble approach for the early detection of breast cancer
Breast Cancer is a significant global health challenge, particularly affecting women with higher mortality compared with other cancer types. Timely...
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Deep Transfer Learning-Based Approach for Glucose Transporter-1 (GLUT1) Expression Assessment
Glucose transporter-1 (GLUT-1) expression level is a biomarker of tumour hypoxia condition in immunohistochemistry (IHC)-stained images. Thus, the...
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An MRI-Based Deep Transfer Learning Radiomics Nomogram to Predict Ki-67 Proliferation Index of Meningioma
The objective of this study was to predict Ki-67 proliferation index of meningioma by using a nomogram based on clinical, radiomics, and deep...
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Deep transfer learning for detection of breast arterial calcifications on mammograms: a comparative study
IntroductionBreast arterial calcifications (BAC) are common incidental findings on routine mammograms, which have been suggested as a sex-specific...
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Recognition of eye diseases based on deep neural networks for transfer learning and improved D-S evidence theory
BackgroundHuman vision has inspired significant advancements in computer vision, yet the human eye is prone to various silent eye diseases. With the...
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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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Detection of developmental dysplasia of the hip in X-ray images using deep transfer learning
BackgroundDevelopmental dysplasia of the hip (DDH) is a relatively common disorder in newborns, with a reported prevalence of 1–5 per 1000 births. It...
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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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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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Colour fusion effect on deep learning classification of uveal melanoma
BackgroundReliable differentiation of uveal melanoma and choroidal nevi is crucial to guide appropriate treatment, preventing unnecessary procedures...
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Deep Learning Approaches for Automatic Quality Assurance of Magnetic Resonance Images Using ACR Phantom
BackgroundIn recent years, there has been a growing trend towards utilizing Artificial Intelligence (AI) and machine learning techniques in medical...
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Deep Learning for Breast MRI Style Transfer with Limited Training Data
In this work we introduce a novel medical image style transfer method, StyleMapper, that can transfer medical scans to an unseen style with access to...
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Deep transfer learning to quantify pleural effusion severity in chest X-rays
PurposeThe detection of pleural effusion in chest radiography is crucial for doctors to make timely treatment decisions for patients with chronic...
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Privacy-Preserving Breast Cancer Classification: A Federated Transfer Learning Approach
Breast cancer is deadly cancer causing a considerable number of fatalities among women in worldwide. To enhance patient outcomes as well as survival...
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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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Detection of K-complexes in EEG waveform images using faster R-CNN and deep transfer learning
BackgroundThe electroencephalography (EEG) signal carries important information about the electrical activity of the brain, which may reveal many...
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Endoscopy Artefact Detection by Deep Transfer Learning of Baseline Models
To visualise the tumours inside the body on a screen, a long and thin tube is inserted with a light source and a camera at the tip to obtain video...
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Distinguishing nontuberculous mycobacterial lung disease and Mycobacterium tuberculosis lung disease on X-ray images using deep transfer learning
BackgroundNontuberculous mycobacterial lung disease (NTM-LD) and Mycobacterium tuberculosis lung disease (MTB-LD) have similar clinical...
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Reducing the number of unnecessary biopsies for mammographic BI-RADS 4 lesions through a deep transfer learning method
BackgroundIn clinical practice, reducing unnecessary biopsies for mammographic BI-RADS 4 lesions is crucial. The objective of this study was to...
