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Combining Generative and Discriminative Representation Learning for Lung CT Analysis With Convolutional Restricted Boltzmann Machines
Open accessAbstract
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.
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Metadata
- Authors
- van Tulder, G., & de Bruijne, M.
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2016-05
- Volume
- 35
- Issue
- 5
- Pages
- 1262-1272
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- 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
- green
- pure.eur.nl
- 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
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