Modeling the Intra-class Variability for Liver Lesion Detection Using a Multi-class Patch-Based CNN
Autor: | Michal Amitai, Jacob Goldberger, Maayan Frid-Adar, Idit Diamant, Eyal Klang, Hayit Greenspan |
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Rok vydání: | 2017 |
Předmět: |
Lesion detection
business.industry Computer science Deep learning ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Boundary (topology) 020207 software engineering Pattern recognition 02 engineering and technology Class (biology) Convolutional neural network Computer aided detection Lesion Liver lesion 0202 electrical engineering electronic engineering information engineering medicine 020201 artificial intelligence & image processing Artificial intelligence medicine.symptom business |
Zdroj: | Patch-Based Techniques in Medical Imaging ISBN: 9783319674339 Patch-MI@MICCAI |
Popis: | Automatic detection of liver lesions in CT images poses a great challenge for researchers. In this work we present a deep learning approach that models explicitly the variability within the non-lesion class, based on prior knowledge of the data, to support an automated lesion detection system. A multi-class convolutional neural network (CNN) is proposed to categorize input image patches into sub-categories of boundary and interior patches, the decisions of which are fused to reach a binary lesion vs non-lesion decision. For validation of our system, we use CT images of 132 livers and 498 lesions. Our approach shows highly improved detection results that outperform the state-of-the-art fully convolutional network. Automated computerized tools, as shown in this work, have the potential in the future to support the radiologists towards improved detection. |
Databáze: | OpenAIRE |
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