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Found 5 papers, 0 papers with code
Deep Learning based Retinal OCT Segmentation
The results show that the proposed methods compare favorably with state of the art techniques, resulting in the smallest mean unsigned error values and associated standard deviations, and performance is comparable with human annotation of retinal layers from OCT when there is only mild retinopathy.
Addressing Artificial Intelligence Bias in Retinal Disease Diagnostics
Using novel generative methods for addressing missing subpopulation training data (DR-referable darker-skin) achieved instead accuracy, for lighter-skin, of 72. 0% (65. 8%, 78. 2%), and for darker-skin, of 71. 5% (65. 2%, 77. 8%), demonstrating closer parity (delta=0. 5%) in accuracy across subpopulations (Welch t-test t=0. 111, P=. 912).
TARA: Training and Representation Alteration for AI Fairness and Domain Generalization
Our experiments also demonstrate the ability of these novel metrics in assessing the Pareto efficiency of the proposed methods.
AI Fairness via Domain Adaptation
While deep learning (DL) approaches are reaching human-level performance for many tasks, including for diagnostics AI, the focus is now on challenges possibly affecting DL deployment, including AI privacy, domain generalization, and fairness.
Triangular Dropout: Variable Network Width without Retraining
One of the most fundamental design choices in neural networks is layer width: it affects the capacity of what a network can learn and determines the complexity of the solution.
