CoChat
Sign Up
Cited by 142Radiomics and Machine Learning in Medical ImagingMedical Image Segmentation TechniquesImage Retrieval and Classification Techniques

The DOI matches its Crossref record; 47 of the 53 references it cites were found in a registry or an index.

Combining Generative and Discriminative Representation Learning for Lung CT Analysis With Convolutional Restricted Boltzmann Machines

Open access
van Tulder, Gijs, de Bruijne, Marleen·2016·IEEE Transactions on Medical Imaging
DOI 10.1109/tmi.2016.2526687
Open this paper in CoChat — free account

Abstract

The choice of features greatly influences the performance of a tissue classification system. Despite this, many systems are built with standard, predefined filter banks that are not optimized for that particular application. Representation learning methods such as restricted Boltzmann machines may outperform these standard filter banks because they learn a feature description directly from the training data. Like many other representation learning methods, restricted Boltzmann machines are unsupervised and are trained with a generative learning objective; this allows them to learn representations from unlabeled data, but does not necessarily produce features that are optimal for classification. In this paper we propose the convolutional classification restricted Boltzmann machine, which combines a generative and a discriminative learning objective. This allows it to learn filters that are good both for describing the training data and for classification. We present experiments with feature learning for lung texture classification and airway detection in CT images. In both applications, a combination of learning objectives outperformed purely discriminative or generative learning, increasing, for instance, the lung tissue classification accuracy by 1 to 8 percentage points. This shows that discriminative learning can help an otherwise unsupervised feature learner to learn filters that are optimized for classification.

Overview

Integrity signals

Registry

Crossref · 10.1109/tmi.2016.2526687

Cites

47 of 47 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Show all 53 references

Research works printed but not linked: 2

  1. roth, 2014, A new 2.5D representation for lymph node detection using random sets of deep convolutional neural network observations, Proc MICCAI, 520
  2. desjardins, 2008, Empirical evaluation of convolutional RBMs for vision

Cited by

Sorted by citations

Items 101–142 of 142

Related

See all

Metadata

Authors
van Tulder, G., & de Bruijne, M.
Venue
IEEE Transactions on Medical Imaging
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2016-05
Volume
35
Issue
5
Pages
1262-1272
ISSN
  • 0278-0062 (ISSN-L)
  • 1558-254X
DOI
10.1109/tmi.2016.2526687
OpenAlex
W2343973580
Cited by
142
References
53
Citations by year
2026: 3 · 2025: 3 · 2024: 6 · 2023: 10 · 2022: 16 · 2021: 18 · 2020: 20 · 2019: 31 · 2018: 13 · 2017: 16 · 2016: 6
FWCI
19.13
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Transactions on Medical Imaging (publishedVersion)
  • PubMed (publishedVersion)
  • EUR Research Repository (Erasmus University Rotterdam) (publishedVersion, other-oa)
  • EUR Research Repository (Erasmus University Rotterdam) (submittedVersion)
  • RePub (Erasmus University Rotterdam) (submittedVersion, other-oa)
  • Research at the University of Copenhagen (University of Copenhagen) (submittedVersion)
  • RePub (Erasmus University Rotterdam) (submittedVersion)
Funders
Nederlandse Organisatie voor Wetenschappelijk Onderzoek (639.022.010)
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
Keywords
convolutional restricted Boltzmann machine, representation learning, lung tissue classification, lung CT, unsupervised feature learning, texture classification
MeSH terms
Machine Learning, Algorithms, Humans, Image Processing, Computer-Assisted, Lung, Tomography, X-Ray Computed, Neural Networks, Computer

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review