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Found 8 papers, 5 papers with code
Rethinking Self-training for Semi-supervised Landmark Detection: A Selection-free Approach
Pseudo pretraining and shrink regression are two essential components for such a curriculum, where the former is the first task of the curriculum for providing a better model initialization and the latter is further added in the later rounds to directly leverage the pseudo-labels in a coarse-to-fine manner.
Unpaired Optical Coherence Tomography Angiography Image Super-Resolution via Frequency-Aware Inverse-Consistency GAN
For optical coherence tomography angiography (OCTA) images, a limited scanning rate leads to a trade-off between field-of-view (FOV) and imaging resolution.
Unsupervised Domain Adaptation for Anatomical Landmark Detection
The framework leverages self-training and domain adversarial learning to address the domain gap during adaptation.
Anatomical Landmark Detection
Unsupervised Domain Adaptation
PromptMRG: Diagnosis-Driven Prompts for Medical Report Generation
To address these challenges, we propose diagnosis-driven prompts for medical report generation (PromptMRG), a novel framework that aims to improve the diagnostic accuracy of MRG with the guidance of diagnosis-aware prompts.
Towards Generalizable Diabetic Retinopathy Grading in Unseen Domains
Diabetic Retinopathy (DR) is a common complication of diabetes and a leading cause of blindness worldwide.
DRAC: Diabetic Retinopathy Analysis Challenge with Ultra-Wide Optical Coherence Tomography Angiography Images
Computer-assisted automatic analysis of diabetic retinopathy (DR) is of great importance in reducing the risks of vision loss and even blindness.
Image Quality-aware Diagnosis via Meta-knowledge Co-embedding
Medical images usually suffer from image degradation in clinical practice, leading to decreased performance of deep learning-based models.
Learning Robust Representation for Joint Grading of Ophthalmic Diseases via Adaptive Curriculum and Feature Disentanglement
However, prior works either grade DR or DME independently, without considering internal correlations between them, or grade them jointly by shared feature representation, yet ignoring potential generalization issues caused by difficult samples and data bias.
