Chest pathology identification using deep feature selection with non-medical training
| Autor: | Sivan Lieberman, Hayit Greenspan, Lior Wolf, Eli Konen, Yaniv Bar, Idit Diamant |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Pathology
medicine.medical_specialty Biomedical Engineering Computational Mechanics Feature selection 02 engineering and technology Machine learning computer.software_genre Convolutional neural network 030218 nuclear medicine & medical imaging Set (abstract data type) 03 medical and health sciences 0302 clinical medicine Robustness (computer science) 0202 electrical engineering electronic engineering information engineering Medicine Radiology Nuclear Medicine and imaging business.industry Deep learning Pattern recognition Computer Science Applications Identification (information) Computer-aided diagnosis Feature (computer vision) 020201 artificial intelligence & image processing Artificial intelligence business computer |
| Zdroj: | Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization. 6:259-263 |
| ISSN: | 2168-1171 2168-1163 |
| DOI: | 10.1080/21681163.2016.1138324 |
| Popis: | We demonstrate the feasibility of detecting pathology in chest X-rays using deep learning approaches based on non-medical learning. Convolutional neural networks (CNN) learn higher level image representations. In this work, we explore the features extracted from layers of the CNN along with a set of classical features, including GIST and bag-of-words. We show results of classification using each feature set as well as fusing among the features. Finally, we perform feature selection on the collection of features to show the most informative feature set for the task. Results of 0.78–0.95 AUC for various pathologies are shown on a data-set of more than 600 radiographs. This study shows the strength and robustness of the CNN features. We conclude that deep learning with large-scale non- medical image databases may be a good substitute, or addition to domain-specific representations which are yet to be available for general medical image recognition tasks. |
| Databáze: | OpenAIRE |
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