Prof. Philipp Rostalski Profile

Prof. Philipp Rostalski

at Univ. zu Lübeck

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 4 April 2022 Poster + Paper

Performance evaluation of lightweight convolutional neural networks on retinal lesion segmentation

M. Siebert, P. Rostalski

Proceedings Volume 12033, 1203334 (2022) https://doi.org/10.1117/12.2611796

KEYWORDS: Image segmentation, RGB color model, Machine learning, Mobile devices

Read Abstract +

In addition to the recent development of deep learning-based, automatic detection systems for diabetic retinopathy (DR), efforts are being made to integrate those systems into mobile detection devices running on the edge requiring lightweight algorithms. Moreover, to enable clinical deployment it is important to enhance the transparency of the deep learning systems usually being black-box models and hence giving no insights into its reasoning. By providing precise segmentation masks for lesions being related to the severity of DR, a good intuition about the decision making of the diagnosing system can be given. Hence, to enable transparent mobile DR detection devices simultaneously segmenting disease-related lesions and running on the edge, lightweight models capable to produce fine-grained segmentation masks are required contradicting the generally high complexity of fully convolutional architectures used for image segmentation. In this paper, we evaluate both the runtime and segmentation performance of several lightweight fully convolutional networks for DR related lesion segmentation and assess its potential to extend mobile DR-grading systems for improved transparency. To this end, the U²-Net is downscaled to reduce the computational load by reducing feature size and applying depthwise separable convolutions and evaluated using deep model ensembling as well as single- and multi-task inference to improve performance and further reduce memory cost. Experimental results using the U²-Net-S† ensemble show good segmentation performance while maintaining a small memory footprint as well as reasonable inference speed and thus indicate a promising first step towards a holistic mobile diagnostic system providing both precise lesion segmentation and DR-grading.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

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

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years