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Delving into masked autoencoders for multi-label thorax disease classification
Abstract Vision Transformer (ViT) has become one of the most popular neural architectures
due to its simplicity, scalability, and compelling performance in multiple vision tasks …
due to its simplicity, scalability, and compelling performance in multiple vision tasks …
Application of artificial intelligence in pancreas endoscopic ultrasound imaging-A systematic review
The pancreas is a vital organ in digestive system which has significant health implications. It
is imperative to evaluate and identify malignant pancreatic lesions promptly in light of the …
is imperative to evaluate and identify malignant pancreatic lesions promptly in light of the …
Fairdomain: Achieving fairness in cross-domain medical image segmentation and classification
Addressing fairness in artificial intelligence (AI), particularly in medical AI, is crucial for
ensuring equitable healthcare outcomes. Recent efforts to enhance fairness have …
ensuring equitable healthcare outcomes. Recent efforts to enhance fairness have …
Maefe: Masked autoencoders family of electrocardiogram for self-supervised pretraining and transfer learning
Electrocardiogram (ECG) is a universal diagnostic tool for heart disease, which can provide
data for deep learning. The scarcity of labeled data is a major challenge for medical artificial …
data for deep learning. The scarcity of labeled data is a major challenge for medical artificial …
Research and application of Transformer based anomaly detection model: A literature review
Transformer, as one of the most advanced neural network models in Natural Language
Processing (NLP), exhibits diverse applications in the field of anomaly detection. To inspire …
Processing (NLP), exhibits diverse applications in the field of anomaly detection. To inspire …
Exploiting structural consistency of chest anatomy for unsupervised anomaly detection in radiography images
Radiography imaging protocols focus on particular body regions, therefore producing
images of great similarity and yielding recurrent anatomical structures across patients …
images of great similarity and yielding recurrent anatomical structures across patients …
Self-supervised pseudo multi-class pre-training for unsupervised anomaly detection and segmentation in medical images
Unsupervised anomaly detection (UAD) methods are trained with normal (or healthy)
images only, but during testing, they are able to classify normal and abnormal (or disease) …
images only, but during testing, they are able to classify normal and abnormal (or disease) …
Two-stage reverse knowledge distillation incorporated and self-supervised masking strategy for industrial anomaly detection
In recent years, unsupervised anomaly detection based on knowledge distillation has
gained special attention and some promising results have been reported in the literature …
gained special attention and some promising results have been reported in the literature …
Counterfactual condition diffusion with continuous prior adaptive correction for anomaly detection in multimodal brain mri
X Chen, Y Peng - Expert Systems with Applications, 2024 - Elsevier
Pixel-level prediction of early lesions is important for disease treatment and saving patients'
lives. Owing to the heterogeneity of pathological brain structures and the complexity of brain …
lives. Owing to the heterogeneity of pathological brain structures and the complexity of brain …
Edmae: An efficient decoupled masked autoencoder for standard view identification in pediatric echocardiography
This paper introduces the Efficient Decoupled Masked Autoencoder (EDMAE), a novel self-
supervised method for recognizing standard views in pediatric echocardiography. EDMAE …
supervised method for recognizing standard views in pediatric echocardiography. EDMAE …