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Found 6 papers, 1 papers with code
Semi-Supervised Semantic Segmentation of Cell Nuclei via Diffusion-based Large-Scale Pre-Training and Collaborative Learning
A series of out-of-distribution tests further confirmed the generality of our framework.
A Test Statistic Estimation-based Approach for Establishing Self-interpretable CNN-based Binary Classifiers
The model is trained to estimate the test statistic of the given trained black-box deep binary classifier to maintain a similar accuracy.
De-speckling of Optical Coherence Tomography Images Using Anscombe Transform and a Noisier2noise Model
Also, sometimes the experiments are demonstrated by simulating noises on clean synthetic images, which is not a realistic scenario.
Uncertainty aware and explainable diagnosis of retinal disease
Deep learning methods for ophthalmic diagnosis have shown considerable success in tasks like segmentation and classification.
Explainable deep learning models in medical image analysis
Deep learning methods have been very effective for a variety of medical diagnostic tasks and has even beaten human experts on some of those.
Application of Deep Learning in Fundus Image Processing for Ophthalmic Diagnosis -- A Review
An overview of the applications of deep learning in ophthalmic diagnosis using retinal fundus images is presented.
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