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Real-world datasets are often imbalanced, with an important class having many fewer examples than other classes. In medical data, normal examples typically greatly outnumber disease examples. A classifier learned from imbalanced data, will tend to be very good at the predicting examples in the larger (normal) class, yet the smaller (disease) class is typically of more interest. Imbalance is dealt...
The goal of this paper is to show the use of data mining techniques to predict the Soft Tissue Sarcoma (STS) tumor progression. STS are cancers which occur in different parts of the body such as fat, muscle and nerves. The lack of effective treatments and the difficulty in predicting treatment response make them challenging for physicians, and has likely slowed the evolution of new therapeutic agents...
Finding appropriate feature representations from radiological images is a vital task for prediction and diagnosis. Deep convolutional neural networks have recently achieved state-of-the-art performance in classification problems from several different domains. Research has also shown the feasibility of using a pre-trained deep neural network as a feature extractor when only a small dataset is available...
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