Adaptive Multi-Region Network For Medical Image Analysis
| Autor: | Yang Gao, Latifur Khan, Zhuoyi Wang, Yigong Wang, Hemeng Tao |
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| Rok vydání: | 2020 |
| Předmět: |
Similarity (geometry)
Artificial neural network Computer science business.industry Deep learning ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Context (language use) Machine learning computer.software_genre 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Key (cryptography) Artificial intelligence Focus (optics) business computer 030217 neurology & neurosurgery |
| Zdroj: | ICIP |
| DOI: | 10.1109/icip40778.2020.9191155 |
| Popis: | Automated diagnosis of significant abnormalities (or lesions) from radiology images has been well exploited in Deep Learning (DL) because of the ability to model sophisticated features. However, a deep neural network should be trained on a huge amount of data to infer the parameter values. Unfortunately, for the problems in lesion diagnosis, there is only a limited amount of data annotated in a manner that is suitable to learn powerful deep models. Moreover, the lesion in the radiology image is often vague and hard to identify without expert knowledge. In this paper, we focus on previous challenges in the automated diagnosis and propose the approach named Adaptive Multi-region Network (AdapNet). The key idea is that we adaptively encode the similarity of lesions in different context regions through margin-max learning strategy, which incorporates the metrics learned on those regions to enhance the effectiveness of the model. Our experiments show that the proposed method can effectively obtain superior performance compared to the existing methods, on the DeepLesion data sets. |
| Databáze: | OpenAIRE |
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