Dr. Mazen Sudah Profile

Dr. Mazen Sudah

at Kuopio Univ. Hospital

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 | 7 April 2023 Paper

Predicting cell type counts in whole slide histology images using evidential multi-task learning

Naga Raju Gudhe, Mazen Sudah, Arto Mannermaa, Veli-Matti Kosma, Hamid Behravan

Proceedings Volume 12471, 124710Z (2023) https://doi.org/10.1117/12.2654117

KEYWORDS: Image segmentation, Pathology, Feature extraction, Data modeling, Convolution, Computed tomography, Semantics, Image resolution

Read Abstract +

Predicting the presence and counting various cell types in the tumor micro-environment is crucial for identifying cell spatial and morphological features in computational pathology. Deep learning (DL) approaches have been used extensively in predicting and counting different cell types in whole slide images (WSIs). However, DL-based probabilistic approaches have limitations in classifying pixels and determining cellular boundaries. In this study, we propose an end-to-end evidential multi-task deep neural network for counting different cell types in hematoxylin and eosin-stained digital pathology images. The aim is to achieve a reasonable accuracy with lower uncertainties than state-of-the-art methods. Our proposed approach consists of one encoder and three task-specific decoders. The encoder utilizes a pre-trained ResNet50 model with ImageNet weights to extract contextual and spatial imaging features. The multi-tasking approach comprises of three tasks: predicting various cell type counts (main task), nuclei semantic segmentation and estimating cell-type density maps (auxiliary tasks). We evaluated the effectiveness of our proposed approach on two open-source datasets, PanNuke and MoNuSAC, which consist of 205,343 and 46,909 nuclei segmentation labels, respectively. Our results were compared with state-of-the-art methods, HoVer-Net, StarDist, and ALBRT. Our proposed approach showed superior performance in cell-type counting compared to HoVer-Net, ALBRT, and StarDist with relative improvements of 11%, 7%, and 5% on the PanNuke dataset test set, and 8.5%, 2%, and 11% on the MoNuSAC dataset test set, respectively, in terms of R². Our experiments suggest that our proposed approach can provide compelling interpretations of diverse cell types and is applicable for various downstream tasks in computational pathology.

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