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Self-supervised pre-training for joint optic disc and cup segmentation via attention-aware network
Image segmentation is a fundamental task in deep learning, which is able to analyse the essence of the images for further development. However, for...
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CheSS: Chest X-Ray Pre-trained Model via Self-supervised Contrastive Learning
Training deep learning models on medical images heavily depends on experts’ expensive and laborious manual labels. In addition, these images, labels,...
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Evaluation of Effectiveness of Self-Supervised Learning in Chest X-Ray Imaging to Reduce Annotated Images
A significant challenge in machine learning-based medical image analysis is the scarcity of medical images. Obtaining a large number of labeled...
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Supervised Exercise Therapy Reduces Presenteeism to Greater Extent Than Unsupervised Self-Care in Workers with Musculoskeletal Pain: a Systematic Review and Meta-Analysis
PurposePresenteeism is defined as the loss of work productivity due to health issues in workers, which can be measured subjectively. This study aimed...
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Self-supervised learning via cluster distance prediction for operating room context awareness
PurposeSemantic segmentation and activity classification are key components to create intelligent surgical systems able to understand and assist...
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Self-supervised learning of accelerometer data provides new insights for sleep and its association with mortality
Sleep is essential to life. Accurate measurement and classification of sleep/wake and sleep stages is important in clinical studies for sleep...
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COVID-19 detection based on self-supervised transfer learning using chest X-ray images
PurposeConsidering several patients screened due to COVID-19 pandemic, computer-aided detection has strong potential in assisting clinical workflow...
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A self-supervised vision transformer to predict survival from histopathology in renal cell carcinoma
PurposeTo develop and validate an interpretable deep learning model to predict overall and disease-specific survival (OS/DSS) in clear cell renal...
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Fast Real-Time Brain Tumor Detection Based on Stimulated Raman Histology and Self-Supervised Deep Learning Model
In intraoperative brain cancer procedures, real-time diagnosis is essential for ensuring safe and effective care. The prevailing workflow, which...
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Assessing the self-reported honesty threshold in adolescent epidemiological research: comparing supervised machine learning and inferential statistical techniques
BackgroundEpidemiological surveys offer essential data on adolescent substance use. Nevertheless, the precision of these self-report-based surveys...
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Wearable based monitoring and self-supervised contrastive learning detect clinical complications during treatment of Hematologic malignancies
Serious clinical complications (SCC; CTCAE grade ≥ 3) occur frequently in patients treated for hematological malignancies. Early diagnosis and...
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ImSpect: Image-driven self-supervised learning for surgical margin evaluation with mass spectrometry
PurposeReal-time assessment of surgical margins is critical for favorable outcomes in cancer patients. The iKnife is a mass spectrometry device that...
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Physiotherapy Rehabilitation Post Patellar Dislocation (PRePPeD)—protocol for an external pilot randomised controlled trial and qualitative study comparing supervised versus self-managed rehabilitation for people after acute patellar dislocation
BackgroundPatellar dislocations mainly affect adolescents and young adults. After this injury, patients are usually referred to physiotherapy for...
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MR-self Noise2Noise: self-supervised deep learning–based image quality improvement of submillimeter resolution 3D MR images
ObjectivesThe study aimed to develop a deep neural network (DNN)–based noise reduction and image quality improvement by only using routine clinical...
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Self-supervised representation learning for surgical activity recognition
Purpose: Virtual reality-based simulators have the potential to become an essential part of surgical education. To make full use of this potential,...
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Approximating Intermediate Feature Maps of Self-Supervised Convolution Neural Network to Learn Hard Positive Representations in Chest Radiography
Recent advances in contrastive learning have significantly improved the performance of deep learning models. In contrastive learning of medical...
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A self-supervised learning strategy for postoperative brain cavity segmentation simulating resections
PurposeAccurate segmentation of brain resection cavities (RCs) aids in postoperative analysis and determining follow-up treatment. Convolutional...
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Domain adaptation and self-supervised learning for surgical margin detection
PurposeOne in five women who undergo breast conserving surgery will need a second revision surgery due to remaining tumor. The iKnife is a mass...
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Cross-modal self-supervised representation learning for gesture and skill recognition in robotic surgery
PurposeMulti- and cross-modal learning consolidates information from multiple data sources which may offer a holistic representation of complex...
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Self-supervised PET Denoising
PurposeEarly deep-learning-based image denoising techniques mainly focused on a fully supervised model that learns how to generate a clean image from...
