Name
Title | ||
|---|---|---|
U2-Net: A Bayesian U-Net model with epistemic uncertainty feedback for photoreceptor layer segmentation in pathological OCT scans. |
Abstract | ||
|---|---|---|
In this paper, we introduce a Bayesian deep learning based model for segmenting the photoreceptor layer in pathological OCT scans. Our architecture provides accurate segmentations of the photoreceptor layer and produces pixel-wise epistemic uncertainty maps that highlight potential areas of pathologies or segmentation errors. We empirically evaluated this approach in two sets of pathological OCT scans of patients with age-related macular degeneration, retinal vein oclussion and diabetic macular edema, improving the performance of the baseline U-Net both in terms of the Dice index and the area under the precision/recall curve. We also observed that the uncertainty estimates were inversely correlated with the model performance, underlying its utility for highlighting areas where manual inspection/correction might be needed. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/isbi.2019.8759581 | ISBI |
Field | DocType | Volume |
Diabetic macular edema,Uncertainty quantification,Pattern recognition,Computer science,Segmentation,Pathological,Macular degeneration,Artificial intelligence,Deep learning,Bayesian probability | Journal | abs/1901.07929 |
ISSN | Citations | PageRank |
IEEE International Symposium on Biomedical Imaging (ISBI 2019) | 0 | 0.34 |
References | Authors | |
8 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| José Ignacio Orlando | 1 | 70 | 7.28 |
| Philipp Seeböck | 2 | 0 | 0.34 |
| Hrvoje Bogunović | 3 | 200 | 17.85 |
| Sophie Klimscha | 4 | 18 | 3.10 |
| Christoph Grechenig | 5 | 0 | 1.01 |
| Sebastian Waldstein | 6 | 80 | 8.52 |
| Bianca Gerendas | 7 | 24 | 5.67 |
| Ursula Schmidt-Erfurth | 8 | 90 | 11.43 |