Titlebook: Mathematical and Computational Oncology; Second International George Bebis,Max Alekseyev,Maria Rodriguez Martine Conference proceedings 202

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书目名称Mathematical and Computational Oncology影响因子(影响力)




书目名称Mathematical and Computational Oncology影响因子(影响力)学科排名




书目名称Mathematical and Computational Oncology网络公开度




书目名称Mathematical and Computational Oncology网络公开度学科排名




书目名称Mathematical and Computational Oncology被引频次




书目名称Mathematical and Computational Oncology被引频次学科排名




书目名称Mathematical and Computational Oncology年度引用




书目名称Mathematical and Computational Oncology年度引用学科排名




书目名称Mathematical and Computational Oncology读者反馈




书目名称Mathematical and Computational Oncology读者反馈学科排名

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蚀刻术

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令人心醉

整体

Fine-Tuning Deep Learning Architectures for Early Detection of Oral Cancern pre-trained deep convolutional neural network architectures compared for the task of fine-tuning to a small oral image dataset. Improvements to our previous work were made, with an accuracy of 80.88% achieved and a corresponding sensitivity of 85.71% and specificity of 76.42%.

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tugHall: A Tool to Reproduce Darwinian Evolution of Cancer Cells for Simulation-Based Personalized Mof the model. The variant allele frequencies are used as target data for the analysis. In tugHall 2.1, the Darwinian evolutionary competition amongst different clones is computed due to clone’s death/birth processes. The open-source code is available in the repository ..

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Discriminative Localized Sparse Representations for Breast Cancer Screeningignant. The performance of our method in conjunction with LC-KSVD dictionary learning is evaluated using 10-, 20-, and 30-fold cross validation on the MIAS dataset. Our results indicate that the proposed sparse analyses may be a useful component for breast cancer screening applications.