Standard
Deep Multi-instance Volumetric Image Classification with Extreme Value Distributions. / Tennakoon, Ruwan; Gostar, Amirali K.; Hoseinnezhad, Reza; de-Bruijne, Marleen; Bab-Hadiashar, Alireza.
Computer Vision - ACCV 2018: 14th Asian Conference on Computer Vision, Perth, Australia, December 2–6, 2018, Revised Selected Papers. ed. / Hongdong Li; C.V. Jawahar; Greg Mori; Konrad Schindler. Springer, 2019. p. 590-604 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 11363 LNCS).
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Harvard
Tennakoon, R, Gostar, AK, Hoseinnezhad, R
, de-Bruijne, M & Bab-Hadiashar, A 2019,
Deep Multi-instance Volumetric Image Classification with Extreme Value Distributions. in H Li, CV Jawahar, G Mori & K Schindler (eds),
Computer Vision - ACCV 2018: 14th Asian Conference on Computer Vision, Perth, Australia, December 2–6, 2018, Revised Selected Papers. Springer, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11363 LNCS, pp. 590-604, 14th Asian Conference on Computer Vision, ACCV 2018, Perth, Australia,
02/12/2018.
https://doi.org/10.1007/978-3-030-20893-6_37APA
Tennakoon, R., Gostar, A. K., Hoseinnezhad, R.
, de-Bruijne, M., & Bab-Hadiashar, A. (2019).
Deep Multi-instance Volumetric Image Classification with Extreme Value Distributions. In H. Li, C. V. Jawahar, G. Mori, & K. Schindler (Eds.),
Computer Vision - ACCV 2018: 14th Asian Conference on Computer Vision, Perth, Australia, December 2–6, 2018, Revised Selected Papers (pp. 590-604). Springer. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11363 LNCS
https://doi.org/10.1007/978-3-030-20893-6_37Vancouver
Tennakoon R, Gostar AK, Hoseinnezhad R
, de-Bruijne M, Bab-Hadiashar A.
Deep Multi-instance Volumetric Image Classification with Extreme Value Distributions. In Li H, Jawahar CV, Mori G, Schindler K, editors, Computer Vision - ACCV 2018: 14th Asian Conference on Computer Vision, Perth, Australia, December 2–6, 2018, Revised Selected Papers. Springer. 2019. p. 590-604. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 11363 LNCS).
https://doi.org/10.1007/978-3-030-20893-6_37Author
Tennakoon, Ruwan ; Gostar, Amirali K. ; Hoseinnezhad, Reza ; de-Bruijne, Marleen ; Bab-Hadiashar, Alireza. / Deep Multi-instance Volumetric Image Classification with Extreme Value Distributions. Computer Vision - ACCV 2018: 14th Asian Conference on Computer Vision, Perth, Australia, December 2–6, 2018, Revised Selected Papers. editor / Hongdong Li ; C.V. Jawahar ; Greg Mori ; Konrad Schindler. Springer, 2019. pp. 590-604 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 11363 LNCS).
Bibtex
@inproceedings{dcab21fc7e434735b22b7c352a37307d,
title = "Deep Multi-instance Volumetric Image Classification with Extreme Value Distributions",
abstract = "Predicting the presence of a disease in volumetric images is an essential task in medical imaging. The use of state-of-the-art techniques like deep convolutional neural networks (CNN) for such tasks is challenging due to limited supervised training data and high memory usage. This paper presents a weakly supervised solution that can be used in learning deep CNN features for volumetric image classification. In the proposed method, we use extreme value theory to model the feature distribution of the images without a pathology and use it to identify positive instances in an image that contains pathology. The experimental results show that the proposed method can learn classifiers that have similar performance to a fully supervised method and have significantly better performance in comparison with methods that use fixed number of instances from a positive image.",
keywords = "Intra-retinal fluid, Learning (artificial intelligence), Macular Edema, Medical image processing, Multiple instance learning, OCT images, Optical coherence tomography, Pigment Epithelial Detachment, Retinal fluid classification, ReTOUCH challenge, Sub-retinal fluid, Weak supervision",
author = "Ruwan Tennakoon and Gostar, {Amirali K.} and Reza Hoseinnezhad and Marleen de-Bruijne and Alireza Bab-Hadiashar",
year = "2019",
doi = "10.1007/978-3-030-20893-6_37",
language = "English",
isbn = "9783030208929",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "590--604",
editor = "Hongdong Li and C.V. Jawahar and Greg Mori and Konrad Schindler",
booktitle = "Computer Vision - ACCV 2018",
address = "Switzerland",
note = "14th Asian Conference on Computer Vision, ACCV 2018 ; Conference date: 02-12-2018 Through 06-12-2018",
}
RIS
TY - GEN
T1 - Deep Multi-instance Volumetric Image Classification with Extreme Value Distributions
AU - Tennakoon, Ruwan
AU - Gostar, Amirali K.
AU - Hoseinnezhad, Reza
AU - de-Bruijne, Marleen
AU - Bab-Hadiashar, Alireza
PY - 2019
Y1 - 2019
N2 - Predicting the presence of a disease in volumetric images is an essential task in medical imaging. The use of state-of-the-art techniques like deep convolutional neural networks (CNN) for such tasks is challenging due to limited supervised training data and high memory usage. This paper presents a weakly supervised solution that can be used in learning deep CNN features for volumetric image classification. In the proposed method, we use extreme value theory to model the feature distribution of the images without a pathology and use it to identify positive instances in an image that contains pathology. The experimental results show that the proposed method can learn classifiers that have similar performance to a fully supervised method and have significantly better performance in comparison with methods that use fixed number of instances from a positive image.
AB - Predicting the presence of a disease in volumetric images is an essential task in medical imaging. The use of state-of-the-art techniques like deep convolutional neural networks (CNN) for such tasks is challenging due to limited supervised training data and high memory usage. This paper presents a weakly supervised solution that can be used in learning deep CNN features for volumetric image classification. In the proposed method, we use extreme value theory to model the feature distribution of the images without a pathology and use it to identify positive instances in an image that contains pathology. The experimental results show that the proposed method can learn classifiers that have similar performance to a fully supervised method and have significantly better performance in comparison with methods that use fixed number of instances from a positive image.
KW - Intra-retinal fluid
KW - Learning (artificial intelligence)
KW - Macular Edema
KW - Medical image processing
KW - Multiple instance learning
KW - OCT images
KW - Optical coherence tomography
KW - Pigment Epithelial Detachment
KW - Retinal fluid classification
KW - ReTOUCH challenge
KW - Sub-retinal fluid
KW - Weak supervision
UR - http://www.scopus.com/inward/record.url?scp=85067239094&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-20893-6_37
DO - 10.1007/978-3-030-20893-6_37
M3 - Article in proceedings
AN - SCOPUS:85067239094
SN - 9783030208929
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 590
EP - 604
BT - Computer Vision - ACCV 2018
A2 - Li, Hongdong
A2 - Jawahar, C.V.
A2 - Mori, Greg
A2 - Schindler, Konrad
PB - Springer
T2 - 14th Asian Conference on Computer Vision, ACCV 2018
Y2 - 2 December 2018 through 6 December 2018
ER -
