Stable classification of diabetic structures from incorrectly labeled optical coherence tomography angiography en face images using multi instance learning

Philipp Matten; Julius Scherer; Thomas Schlegl; Jonas Nienhaus; Heiko Stino; Andreas Pollreisz; Wolfgang Drexler; Rainer A. Leitgeb; Tilman Schmoll

doi:10.1117/12.2652540

8 March 2023 Stable classification of diabetic structures from incorrectly labeled optical coherence tomography angiography en face images using multi instance learning

Philipp Matten, Julius Scherer, Thomas Schlegl, Jonas Nienhaus, Heiko Stino, Andreas Pollreisz, Wolfgang Drexler, Rainer A. Leitgeb, Tilman Schmoll

Author Affiliations +

Proceedings Volume 12367, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVII; 123670S (2023) https://doi.org/10.1117/12.2652540
Event: SPIE BiOS, 2023, San Francisco, California, United States

Abstract

We present a multiple instance learning-based network, MIL-ResNet14, detecting biomarkers for diabetic retinopathy in a widefield optical coherence tomography angiography dataset with high accuracy, without the necessity of annotations other than the information of whether a scan stems from a diabetic patient or not. Previously introduced deep learning-based classifiers were able to support the detection of diabetic biomarkers in OCTA images, however, require expert labeling on a pixel-level, a labor-intensive and expensive process. We evaluated our proposed architecture against two proven-capable classifiers, ResNet14 and VGG16. The dataset we applied for this study was acquired with a MHz A-Scan rate widefield Swept Source-OCT device. We utilized a total of 352 en face images, displaying retinal vasculature over a field of view of 18 mm x 18 mm. MIL-ResNet14 outperformed both other networks with an F-score of 0.95, a precision of 0.909 and an Area Under the Curve of 0.973. In addition, we could show via saliency overlays of gradient-weighted class activation mappings onto the en face images, that MIL-ResNet14 pays special attention to clinically relevant biomarkers like ischemic areas and retinal vessel anomalies. This could therefore function as a vigorous diagnostic decision support tool for clinical ophthalmologic screenings.

Conference Presentation

Citation Download Citation

Philipp Matten, Julius Scherer, Thomas Schlegl, Jonas Nienhaus, Heiko Stino, Andreas Pollreisz, Wolfgang Drexler, Rainer A. Leitgeb, and Tilman Schmoll "Stable classification of diabetic structures from incorrectly labeled optical coherence tomography angiography en face images using multi instance learning", Proc. SPIE 12367, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVII, 123670S (8 March 2023); https://doi.org/10.1117/12.2652540

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