Stable classification of diabetic structures from incorrectly labeled OCTA 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:10.1117/12.2652891

5 April 2023 Stable classification of diabetic structures from incorrectly labeled OCTA 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 12464, Medical Imaging 2023: Image Processing; 124641P (2023) https://doi.org/10.1117/12.2652891
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

Previously introduced deep learning classifiers were able to support diabetic biomarker detection in OCTA en face images, but require pixel-by-pixel expert labeling, which is a labor-intensive and expensive process. We present a multiple-instance learning-based network, MIL-ResNet,14 that detects clinically relevant diabetic retinopathy biomarkers in a wide-angle (65°) OCTA dataset with high accuracy without annotation. We evaluated our proposed architecture against two well-established machine learning classifiers, ResNet14 and VGG16. The dataset we used for this study was acquired with a MHz A-scan rate swept source OCT device. We used a total of 352 en face images representing the retinal vasculature over an 18 mm x 18 mm field of view. MIL-ResNet14 outperformed the other two 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 were able to demonstrate that MIL-ResNet14 paid special attention to relevant biomarkers such as ischemic areas and retinal vascular abnormalities by saliency overlay of gradient-weighted class activation maps on top of the en face images. Thus, OCTA could be used as a powerful diagnostic decision support tool for clinical ophthalmic screening in combination with our MIL approach.

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 OCTA en face images using multi instance learning", Proc. SPIE 12464, Medical Imaging 2023: Image Processing, 124641P (5 April 2023); https://doi.org/10.1117/12.2652891

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available