Deep Multi-instance Volumetric Image Classification with Extreme Value Distributions

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

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.

OriginalsprogEngelsk
TitelComputer Vision - ACCV 2018 : 14th Asian Conference on Computer Vision, Perth, Australia, December 2–6, 2018, Revised Selected Papers
RedaktørerHongdong Li, C.V. Jawahar, Greg Mori, Konrad Schindler
Antal sider15
ForlagSpringer
Publikationsdato2019
Sider590-604
ISBN (Trykt)9783030208929
ISBN (Elektronisk)9783030208936
DOI
StatusUdgivet - 2019
Begivenhed14th Asian Conference on Computer Vision, ACCV 2018 - Perth, Australien
Varighed: 2 dec. 20186 dec. 2018

Konference

Konference14th Asian Conference on Computer Vision, ACCV 2018
LandAustralien
ByPerth
Periode02/12/201806/12/2018
NavnLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Vol/bind11363 LNCS
ISSN0302-9743

ID: 223572616