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Systematic Augmentation in HSV Space for Semantic Segmentation of Prostate Biopsies

Winzell, Filip LU ; Arvidsson, Ida LU ; Overgaard, Niels Christian LU ; Åström, Kalle LU orcid ; Marginean, Felicia-Elena LU orcid ; Bjartell, Anders LU ; Krzyzanowska, Agnieszka LU ; Simoulis, Athanasios LU orcid and Heyden, Anders LU orcid (2023) 22nd Scandinavian Conference on Image Analysis, SCIA 2023 In Lecture Notes in Computer Science 13886. p.293-308
Abstract
In recent years, the combination of the digitization of the field of pathology and increased computational power has led to a big increase in research of computer-aided diagnostics using systems based on artificial intelligence (AI). This includes detection and classification of prostate cancer, where several studies have shown great promise in automated prostate cancer grading using deep learning based AI systems. However, there is still work to be done to ensure that these algorithms are invariant to possible variations of the digitized microscopy images they are applied to. A standard method in deep learning to increase the variation of the training data is dataset augmentation. All of these studies apply some augmentation of their... (More)
In recent years, the combination of the digitization of the field of pathology and increased computational power has led to a big increase in research of computer-aided diagnostics using systems based on artificial intelligence (AI). This includes detection and classification of prostate cancer, where several studies have shown great promise in automated prostate cancer grading using deep learning based AI systems. However, there is still work to be done to ensure that these algorithms are invariant to possible variations of the digitized microscopy images they are applied to. A standard method in deep learning to increase the variation of the training data is dataset augmentation. All of these studies apply some augmentation of their data, however, there is a lack of evaluation of different methods and their impact on this crucial part of the AI systems. In this study, we look into different color augmentation methods for the task of segmentation of prostate biopsies. Furthermore, we introduce a novel color augmentation method based on stereographic projection. Our results affirm the importance of studying different augmentation methods and indicate a gain in performance using our method. (Less)
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author
; ; ; ; ; ; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
Image Analysis : 23rd Scandinavian Conference, SCIA 2023, Proceedings, Part II - 23rd Scandinavian Conference, SCIA 2023, Proceedings, Part II
series title
Lecture Notes in Computer Science
volume
13886
pages
293 - 308
publisher
Springer
conference name
22nd Scandinavian Conference on Image Analysis, SCIA 2023
conference location
Sirkka, Finland
conference dates
2023-04-18 - 2023-04-21
external identifiers
  • scopus:85161427190
ISSN
0302-9743
1611-3349
ISBN
978-3-031-31437-7
978-3-031-31438-4
DOI
10.1007/978-3-031-31438-4_20
language
English
LU publication?
yes
id
eb0928b5-b2fc-4209-b1b8-da4d53eab2ab
date added to LUP
2023-04-27 14:33:11
date last changed
2024-06-15 02:14:07
@inproceedings{eb0928b5-b2fc-4209-b1b8-da4d53eab2ab,
  abstract     = {{In recent years, the combination of the digitization of the field of pathology and increased computational power has led to a big increase in research of computer-aided diagnostics using systems based on artificial intelligence (AI). This includes detection and classification of prostate cancer, where several studies have shown great promise in automated prostate cancer grading using deep learning based AI systems. However, there is still work to be done to ensure that these algorithms are invariant to possible variations of the digitized microscopy images they are applied to. A standard method in deep learning to increase the variation of the training data is dataset augmentation. All of these studies apply some augmentation of their data, however, there is a lack of evaluation of different methods and their impact on this crucial part of the AI systems. In this study, we look into different color augmentation methods for the task of segmentation of prostate biopsies. Furthermore, we introduce a novel color augmentation method based on stereographic projection. Our results affirm the importance of studying different augmentation methods and indicate a gain in performance using our method.}},
  author       = {{Winzell, Filip and Arvidsson, Ida and Overgaard, Niels Christian and Åström, Kalle and Marginean, Felicia-Elena and Bjartell, Anders and Krzyzanowska, Agnieszka and Simoulis, Athanasios and Heyden, Anders}},
  booktitle    = {{Image Analysis : 23rd Scandinavian Conference, SCIA 2023, Proceedings, Part II}},
  isbn         = {{978-3-031-31437-7}},
  issn         = {{0302-9743}},
  language     = {{eng}},
  month        = {{04}},
  pages        = {{293--308}},
  publisher    = {{Springer}},
  series       = {{Lecture Notes in Computer Science}},
  title        = {{Systematic Augmentation in HSV Space for Semantic Segmentation of Prostate Biopsies}},
  url          = {{http://dx.doi.org/10.1007/978-3-031-31438-4_20}},
  doi          = {{10.1007/978-3-031-31438-4_20}},
  volume       = {{13886}},
  year         = {{2023}},
}