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Bioimage Classification with Handcrafted and Learned Features
- Publication Year :
- 2019
- Publisher :
- Institute of Electrical and Electronics Engineers Inc., 2019.
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Abstract
- Bioimage classification is increasingly becoming more important in many biological studies including those that require accurate cell phenotype recognition, subcellular localization, and histopathological classification. In this paper, we present a new General Purpose (GenP) bioimage classification method that can be applied to a large range of classification problems. The GenP system we propose is an ensemble that combines multiple texture features (both handcrafted and learned descriptors) for superior and generalizable discriminative power. Our ensemble obtains a boosting of performance by combining local features, dense sampling features, and deep learning features. Each descriptor is used to train a different Support Vector Machine that is then combined by sum rule. We evaluate our method on a diverse set of bioimage classification tasks each represented by a benchmark database, including some of those available in the IICBU 2008 database. Each bioimage classification task represents a typical subcellular, cellular, and tissue level classification problem. Our evaluation on these datasets demonstrates that the proposed GenP bioimage ensemble obtains state-of-the-art performance without any ad-hoc dataset tuning of the parameters (thereby avoiding any risk of overfitting/overtraining). To reproduce the experiments reported in this paper, the MATLAB code of all the descriptors is available at https://github.com/LorisNanni and https://www.dropbox.com/s/bguw035yrqz0pwp/ElencoCode.docx?dl=0.
- Subjects :
- Boosting (machine learning)
Support Vector Machine
Computer science
0206 medical engineering
Feature extraction
Biomedical imaging
Classification
Databases
Deep Learning
Machine learning
Microscopy imaging
Support vector machines
Task analysis
Training
Biotechnology
Genetics
Applied Mathematics
02 engineering and technology
Overfitting
Discriminative model
Image texture
Contextual image classification
business.industry
Deep learning
Pattern recognition
Support vector machine
ComputingMethodologies_PATTERNRECOGNITION
Artificial intelligence
business
020602 bioinformatics
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....17f8a11485dc6d63e392e3d08235dd9e