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Found 5 papers, 4 papers with code
PDL: Regularizing Multiple Instance Learning with Progressive Dropout Layers
Multiple instance learning (MIL) was a weakly supervised learning approach that sought to assign binary class labels to collections of instances known as bags.
Multiple Instance Learning
Weakly Supervised Classification
+3
nnMobileNet: Rethinking CNN for Retinopathy Research
Over the past few decades, convolutional neural networks (CNNs) have been at the forefront of the detection and tracking of various retinal diseases (RD).
OTRE: Where Optimal Transport Guided Unpaired Image-to-Image Translation Meets Regularization by Enhancing
Non-mydriatic retinal color fundus photography (CFP) is widely available due to the advantage of not requiring pupillary dilation, however, is prone to poor quality due to operators, systemic imperfections, or patient-related causes.
Optimal Transport Guided Unsupervised Learning for Enhancing low-quality Retinal Images
In this paper, we proposed a simple but effective end-to-end framework for enhancing poor-quality retinal fundus images.
Self-Supervised Equivariant Regularization Reconciles Multiple Instance Learning: Joint Referable Diabetic Retinopathy Classification and Lesion Segmentation
Lesion appearance is a crucial clue for medical providers to distinguish referable diabetic retinopathy (rDR) from non-referable DR.
