Deep Multi-instance Volumetric Image Classification with Extreme Value Distributions

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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.

Original languageEnglish
Title of host publicationComputer Vision - ACCV 2018 : 14th Asian Conference on Computer Vision, Perth, Australia, December 2–6, 2018, Revised Selected Papers
EditorsHongdong Li, C.V. Jawahar, Greg Mori, Konrad Schindler
Number of pages15
PublisherSpringer
Publication date2019
Pages590-604
ISBN (Print)9783030208929
ISBN (Electronic)9783030208936
DOIs
Publication statusPublished - 2019
Event14th Asian Conference on Computer Vision, ACCV 2018 - Perth, Australia
Duration: 2 Dec 20186 Dec 2018

Conference

Conference14th Asian Conference on Computer Vision, ACCV 2018
LandAustralia
ByPerth
Periode02/12/201806/12/2018
SeriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11363 LNCS
ISSN0302-9743

    Research areas

  • 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

ID: 223572616