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Maximum mean discrepancy kernels for predictive and prognostic modeling of whole slide images
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Keller, Piotr, Dawood, Muhammad and Minhas, Fayyaz ul Amir Afsar (2023) Maximum mean discrepancy kernels for predictive and prognostic modeling of whole slide images. In: 20th IEEE International Symposium on Biomedical Imaging, Cartagena de Indias, Colombia, 18-21 Apr 2023 (In Press)
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Abstract
How similar are two images? In computational pathology, where Whole Slide Images (WSIs) of digitally scanned tissue samples from patients can be multi-gigapixels in size, determination of degree of similarity between two WSIs is a challenging task with a number of practical applications. In this work, we explore a novel strategy based on kernelized Maximum Mean Discrepancy (MMD) analysis for determination of pairwise similarity between WSIs. The proposed approach works by calculating MMD between two WSIs using kernels over deep features of image patches. This allows representation of an entire dataset of WSIs as a kernel matrix for WSI level clustering, weakly-supervised prediction of TP53 mutation status in breast cancer patients from their routine WSIs as well as survival analysis with state of the art prediction performance. We believe that this work will open up further avenues for application of WSI-level kernels for predictive and prognostic tasks in computational pathology.
Item Type: | Conference Item (Paper) | ||||||||||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software R Medicine > RB Pathology R Medicine > RC Internal medicine T Technology > TA Engineering (General). Civil engineering (General) |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||||||||
Library of Congress Subject Headings (LCSH): | Image processing -- Digital techniques, Pathology -- Data processing, Histology, Pathological , Diagnostic imaging -- Data processing | ||||||||||||
Publisher: | IEEE | ||||||||||||
Official Date: | 2023 | ||||||||||||
Dates: |
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Status: | Peer Reviewed | ||||||||||||
Publication Status: | In Press | ||||||||||||
Reuse Statement (publisher, data, author rights): | © 2023. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||||||
Date of first compliant deposit: | 21 April 2023 | ||||||||||||
Date of first compliant Open Access: | 26 April 2023 | ||||||||||||
RIOXX Funder/Project Grant: |
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Conference Paper Type: | Paper | ||||||||||||
Title of Event: | 20th IEEE International Symposium on Biomedical Imaging | ||||||||||||
Type of Event: | Conference | ||||||||||||
Location of Event: | Cartagena de Indias, Colombia | ||||||||||||
Date(s) of Event: | 18-21 Apr 2023 | ||||||||||||
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Open Access Version: |
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