The Library
SAFRON : Stitching Across the Frontier Network for generating colorectal cancer histology images
Tools
Deshpande, Srijay, Minhas, Fayyaz ul Amir Afsar, Graham, Simon and Rajpoot, Nasir M. (Nasir Mahmood) (2022) SAFRON : Stitching Across the Frontier Network for generating colorectal cancer histology images. Medical Image Analysis, 77 . 102337. doi:10.1016/j.media.2021.102337 ISSN 1361-8415.
Research output not available from this repository.
Request-a-Copy directly from author or use local Library Get it For Me service.
Official URL: https://doi.org/10.1016/j.media.2021.102337
Abstract
Automated synthesis of histology images has several potential applications including the development of data-efficient deep learning algorithms. In the field of computational pathology, where histology images are large in size and visual context is crucial, synthesis of large high-resolution images via generative modeling is an important but challenging task due to memory and computational constraints. To address this challenge, we propose a novel framework called SAFRON (Stitching Across the FROntier Network) to construct realistic, large high-resolution tissue images conditioned on input tissue component masks. The main novelty in the framework is integration of stitching in its loss function which enables generation of images of arbitrarily large sizes after training on relatively small image patches while preserving morphological features with minimal boundary artifacts. We have used the proposed framework for generating, to the best of our knowledge, the largest-sized synthetic histology images to date (up to 11K×8K pixels). Compared to existing approaches, our framework is efficient in terms of the memory required for training and computations needed for synthesizing large high-resolution images. The quality of generated images was assessed quantitatively using Frechet Inception Distance as well as by 7 trained pathologists, who assigned a realism score to a set of images generated by SAFRON. The average realism score across all pathologists for synthetic images was as high as that of real images. We also show that training with additional synthetic data generated by SAFRON can significantly boost prediction performance of gland segmentation and cancer detection algorithms in colorectal cancer histology images. [Abstract copyright: Copyright © 2021. Published by Elsevier B.V.]
Item Type: | Journal Article | ||||||||
---|---|---|---|---|---|---|---|---|---|
Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||||
SWORD Depositor: | Library Publications Router | ||||||||
Journal or Publication Title: | Medical Image Analysis | ||||||||
Publisher: | Elsevier Science BV | ||||||||
ISSN: | 1361-8415 | ||||||||
Official Date: | April 2022 | ||||||||
Dates: |
|
||||||||
Volume: | 77 | ||||||||
Article Number: | 102337 | ||||||||
DOI: | 10.1016/j.media.2021.102337 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Reuse Statement (publisher, data, author rights): | ** From PubMed via Jisc Publications Router ** History: received 17-03-2021; revised 13-10-2021; accepted 14-12-2021. | ||||||||
Access rights to Published version: | Restricted or Subscription Access |
Request changes or add full text files to a record
Repository staff actions (login required)
View Item |