Unsupervised Learning in PET Radiomics
Unsupervised Learning in PET Radiomics
In this study, we investigated large scale radoimics on 116 breast cancer patients. We are particularly interested in unsupervised learning to bicluster patients and features in order to associate such biclusters with the disease characteristics. The results show that radiomics features with wavelet features have a better biclustering ability. And 172 radiomics features have shown a better classification capability.
- University of California System United States
- Florida Institute of Technology United States
- University of California, San Francisco United States
Radiomics, 000, Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Unsupervised Clustering, Workflow, Good Health and Well Being, PET, Information and Computing Sciences, 616, Breast Cancer, Women's Health, Biomedical Imaging, Cancer
Radiomics, 000, Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Unsupervised Clustering, Workflow, Good Health and Well Being, PET, Information and Computing Sciences, 616, Breast Cancer, Women's Health, Biomedical Imaging, Cancer
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