Detecting Bone Lesions in Multiple Myeloma Patients Using Transfer Learning
| Autor: | Johannes Hofmanninger, Marc-André Weber, Matthias Perkonigg, Georg Langs, Björn H. Menze |
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| Rok vydání: | 2018 |
| Předmět: |
medicine.diagnostic_test
Channel (digital image) Computer science business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Computed tomography Pattern recognition medicine.disease Convolutional neural network Imaging data Bone lesion medicine Artificial intelligence business Transfer of learning Multiple myeloma ComputingMethodologies_COMPUTERGRAPHICS |
| Zdroj: | Data Driven Treatment Response Assessment and Preterm, Perinatal, and Paediatric Image Analysis ISBN: 9783030008062 DATRA/PIPPI@MICCAI |
| Popis: | The detection of bone lesions is important for the diagnosis and staging of multiple myeloma patients. The scarce availability of annotated data renders training of automated detectors challenging. Here, we present a transfer learning approach using convolutional neural networks to detect bone lesions in computed tomography imaging data. We compare different learning approaches, and demonstrate that pretraining a convolutional neural network on natural images improves detection accuracy. Also, we describe a patch extraction strategy which encodes different information into each input channel of the networks. We train and evaluate our approach on a dataset with 660 annotated bone lesions, and show how the resulting marker map high-lights lesions in computed tomography imaging data. |
| Databáze: | OpenAIRE |
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