The Library
ALBRT : cellular composition prediction in routine histology images
Tools
Dawood, Muhammad, Branson, Kim, Rajpoot, Nasir M. and Minhas, Fayyaz ul Amir Afsar (2021) ALBRT : cellular composition prediction in routine histology images. In: IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), Montreal, BC, Canada, 11-17 Oct 2021 pp. 664-673. ISBN 9781665401913. doi:10.1109/ICCVW54120.2021.00080 ISSN 2473-9944. (In Press)
|
PDF
WRAP-ALBRT-cellular-composition-prediction-routine-histology-images-2021.pdf - Accepted Version - Requires a PDF viewer. Download (2436Kb) | Preview |
Official URL: http://dx.doi.org/10.1109/ICCVW54120.2021.00080
Abstract
Cellular composition prediction, i.e., predicting the presence and counts of different types of cells in the tumor microenvironment from a digitized image of a Hematoxylin and Eosin (H&E) stained tissue section can be used for various tasks in computational pathology such as the analysis of cellular topology and interactions, subtype prediction, survival analysis, etc. In this work, we propose an image-based cellular composition predictor (ALBRT) which can accurately predict the presence and counts of different types of cells in a given image patch. ALBRT, by its contrastive-learning inspired design, learns a compact and rotation-invariant feature representation that is then used for cellular composition prediction of different cell types. It offers significant improvement over existing state-of-the-art approaches for cell classification and counting. The patch-level feature representation learned by ALBRT is transferrable for cellular composition analysis over novel datasets and can also be utilized for downstream prediction tasks in CPath as well. The code and the inference web-server for the proposed method are available at the URL: https://github.com/engrodawood/ALBRT.
Item Type: | Conference Item (Paper) | ||||||
---|---|---|---|---|---|---|---|
Subjects: | R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer) | ||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||
Library of Congress Subject Headings (LCSH): | Diagnostic imaging -- Data processing, Histology, Pathological, Tumors, Histology, Pathology, Cellular, Image processing -- Digital techniques, Imaging systems in medicine, Cells, Cell aggregation, Computer vision | ||||||
Publisher: | IEEE | ||||||
ISBN: | 9781665401913 | ||||||
ISSN: | 2473-9944 | ||||||
Book Title: | 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) | ||||||
Official Date: | 2021 | ||||||
Dates: |
|
||||||
Page Range: | pp. 664-673 | ||||||
DOI: | 10.1109/ICCVW54120.2021.00080 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | In Press | ||||||
Re-use Statement: | © 2021 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: | Open Access (Creative Commons) | ||||||
Date of first compliant deposit: | 30 January 2024 | ||||||
Date of first compliant Open Access: | 30 January 2024 | ||||||
RIOXX Funder/Project Grant: |
|
||||||
Conference Paper Type: | Paper | ||||||
Title of Event: | IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) | ||||||
Type of Event: | Conference | ||||||
Location of Event: | Montreal, BC, Canada | ||||||
Date(s) of Event: | 11-17 Oct 2021 | ||||||
Open Access Version: |
Request changes or add full text files to a record
Repository staff actions (login required)
View Item |
Downloads
Downloads per month over past year