Improving CNN Training using Disentanglement for Liver Lesion Classification in CT
| Autor: | Hayit Greenspan, Roey Mechrez, Noa Yedidia, Avi Ben-Cohen |
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| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
Computer science Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition Sample (statistics) 02 engineering and technology 010501 environmental sciences 01 natural sciences Synthetic data Bottleneck 0202 electrical engineering electronic engineering information engineering Medical imaging Humans Representation (mathematics) 0105 earth and related environmental sciences Training set Artificial neural network business.industry Liver Neoplasms Pattern recognition Class (biology) Liver Key (cryptography) 020201 artificial intelligence & image processing Neural Networks Computer Artificial intelligence Tomography X-Ray Computed business Algorithms |
| Zdroj: | EMBC |
| DOI: | 10.48550/arxiv.1811.00501 |
| Popis: | Training data is the key component in designing algorithms for medical image analysis and in many cases it is the main bottleneck in achieving good results. Recent progress in image generation has enabled the training of neural network based solutions using synthetic data. A key factor in the generation of new samples is controlling the important appearance features and potentially being able to generate a new sample of a specific class with different variants. In this work we suggest the synthesis of new data by mixing the class specified and unspecified representation of different factors in the training data which are separated using a disentanglement based scheme. Our experiments on liver lesion classification in CT show an average improvement of 7.4% in accuracy over the baseline training scheme. |
| Databáze: | OpenAIRE |
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