Deep Learning based Retinal OCT Segmentation

no code implementations • 29 Jan 2018 • Mike Pekala, Neil Joshi, David E. Freund, Neil M. Bressler, Delia Cabrera DeBuc, Philippe M. Burlina

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.

regression Segmentation +1

Addressing Artificial Intelligence Bias in Retinal Disease Diagnostics

no code implementations • 28 Apr 2020 • Philippe Burlina, Neil Joshi, William Paul, Katia D. Pacheco, Neil M. Bressler

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).

Domain Generalization