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Self-supervised learning for classifying paranasal anomalies in the maxillary sinus
PurposeParanasal anomalies, frequently identified in routine radiological screenings, exhibit diverse morphological characteristics. Due to the...
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Self-supervised category selective attention classifier network for diabetic macular edema classification
AimsThis study aims to develop an advanced model for the classification of Diabetic Macular Edema (DME) using deep learning techniques. Specifically,...
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A self-supervised classification model for endometrial diseases
PurposeUltrasound imaging is the preferred method for the early diagnosis of endometrial diseases because of its non-invasive nature, low cost, and...
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Self-supervised learning for gastritis detection with gastric X-ray images
PurposeManual annotation of gastric X-ray images by doctors for gastritis detection is time-consuming and expensive. To solve this, a self-supervised...
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Subjective and objective image quality of low-dose CT images processed using a self-supervised denoising algorithm
This study aimed to assess the subjective and objective image quality of low-dose computed tomography (CT) images processed using a self-supervised...
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Self-supervised learning for medical image classification: a systematic review and implementation guidelines
Advancements in deep learning and computer vision provide promising solutions for medical image analysis, potentially improving healthcare and...
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A survey of the impact of self-supervised pretraining for diagnostic tasks in medical X-ray, CT, MRI, and ultrasound
Self-supervised pretraining has been observed to be effective at improving feature representations for transfer learning, leveraging large amounts of...
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Semi-supervised skin cancer diagnosis based on self-feedback threshold focal learning
Worldwide, skin cancer prevalence necessitates accurate diagnosis to alleviate public health burdens. Although the application of artificial...
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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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Clustering single-cell RNA sequencing data via iterative smoothing and self-supervised discriminative embedding
Single-cell transcriptome sequencing (scRNA-seq) is a high-throughput technique used to study gene expression at the single-cell level. Clustering...
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Enhancing diagnostic deep learning via self-supervised pretraining on large-scale, unlabeled non-medical images
BackgroundPretraining labeled datasets, like ImageNet, have become a technical standard in advanced medical image analysis. However, the emergence of...
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Self-supervised learning for human activity recognition using 700,000 person-days of wearable data
Accurate physical activity monitoring is essential to understand the impact of physical activity on one’s physical health and overall well-being....
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WISE: whole-scenario embryo identification using self-supervised learning encoder in IVF
PurposeTo study the effectiveness of whole-scenario embryo identification using a self-supervised learning encoder (WISE) in in vitro fertilization...
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Efficacy of supervised self-reduction vs. physician-assisted techniques for anterior shoulder dislocations: a systematic review and meta-analysis
Background and objectiveReduction manipulation using self-reduction procedures such as Stimson, Milch, and Boss-Holtzach should be easy and effective...
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BarlowTwins-CXR: enhancing chest X-ray abnormality localization in heterogeneous data with cross-domain self-supervised learning
BackgroundChest X-ray imaging based abnormality localization, essential in diagnosing various diseases, faces significant clinical challenges due to...
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An intensity-based self-supervised domain adaptation method for intervertebral disc segmentation in magnetic resonance imaging
Background and objective:Accurate IVD segmentation is crucial for diagnosing and treating spinal conditions. Traditional deep learning methods depend...
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HoopTransformer: Advancing NBA Offensive Play Recognition with Self-Supervised Learning from Player Trajectories
Background and ObjectiveUnderstanding and recognizing basketball offensive set plays, which involve intricate interactions between players, have...
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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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Automated segmentation of lesions and organs at risk on [68Ga]Ga-PSMA-11 PET/CT images using self-supervised learning with Swin UNETR
BackgroundProstate-specific membrane antigen (PSMA) PET/CT imaging is widely used for quantitative image analysis, especially in radioligand therapy...
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Self-supervised attention-based deep learning for pan-cancer mutation prediction from histopathology
The histopathological phenotype of tumors reflects the underlying genetic makeup. Deep learning can predict genetic alterations from pathology...
