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.
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.
Our experiments also demonstrate the ability of these novel metrics in assessing the Pareto efficiency of the proposed methods.
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).
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.