Soft and self constrained clustering for group-based labeling
| Autor: | Hideaki Hayashi, Ryoma Bise, Shota Harada, Kiyohito Tanaka, Seiichi Uchida |
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| Rok vydání: | 2020 |
| Předmět: |
Group based
Computer science Health Informatics computer.software_genre 030218 nuclear medicine & medical imaging Image (mathematics) Set (abstract data type) 03 medical and health sciences 0302 clinical medicine Cluster Analysis Humans Radiology Nuclear Medicine and imaging Cluster analysis Radiological and Ultrasound Technology Contextual image classification Constrained clustering Endoscopy Computer Graphics and Computer-Aided Design Semantics ComputingMethodologies_PATTERNRECOGNITION Deep neural networks Computer Vision and Pattern Recognition Data mining Neural Networks Computer computer 030217 neurology & neurosurgery Endoscopic image |
| Zdroj: | Medical image analysis. 72 |
| ISSN: | 1361-8423 |
| Popis: | When using deep neural networks in medical image classification tasks, it is mandatory to prepare a large-scale labeled image set, and this often requires significant effort by medical experts. One strategy to reduce the labeling cost is group-based labeling, where image samples are clustered and then a label is attached to each cluster. The efficiency of this strategy depends on the purity of the clusters. Constrained clustering is an effective way to improve the purity of the clusters if we can give appropriate must-links and cannot-links as constraints. However, for medical image clustering, the conventional constrained clustering methods encounter two issues. The first issue is that constraints are not always appropriate due to the gap between semantic and visual similarities. The second issue is that attaching constraints requires extra effort from medical experts. To deal with the first issue, we propose a novel soft-constrained clustering method, which has the ability to ignore inappropriate constraints. To deal with the second issue, we propose a self-constrained clustering method that utilizes prior knowledge about the target images to set the constraints automatically. Experiments with the endoscopic image datasets demonstrated that the proposed methods give clustering results with higher purity. |
| Databáze: | OpenAIRE |
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