S. Oeltze-Jafra ,  Vilanova Bartrolí Anna ,  K. Bühler ,  S. Engelhardt ,  N. Pezzotti ,  B. Preim (2018)

Blending Visualization with Data Mining and Machine Learning for Biomedical Data Analysis

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Groundbreaking results are achieved by neural networks in medical image processing. This success is reflected by a high and presum- ably increasing number of Deep Learning related submissions to the MICCAI conference. Despite the enormous progress in this field crucial challenges remain such as understanding the learned features, which often reside in high-dimensional space, as well as the tailor-made design and improvement of neural networks. In this tutorial, we will show how visualization can help in tackling these challenges. In traditional machine learning, features are designed instead of being learned, which is often a tedious process requir- ing skilled experts. We will also show how the feature design can be supported by blending visualization and data mining techniques. Causal networks supporting prognostic reasoning and discovering functional interactions are frequently being learned from complex biomedical and epidemiological data. We will show how visual- ization can assist an exploration and verification of the learned net- works in order to, e.g., remove spurious dependencies. Data mining systematically applies statistical and mathematical models to complex data in order to reveal patterns and trends and eventually, to infer knowledge from data. Many biomedical prob- lems are being addressed using data mining techniques. Impor- tant challenges thereby are providing guidance to data mining lay- men, e.g., physicians, in adjusting the parameters of a data min- ing algorithm and in interpreting its results, e.g., clusters in high- dimensional space or data sub-spaces. Again, visualization has been demonstrated to be beneficial in tackling theses challenges. It assists in understanding the parameter space and cluster structure. In the tutorial, we address the blending of visualization with data mining and machine learning from a research and an application- oriented perspective. The latter focuses on cardiac surgery plan- ning, understand gene-structure behavior in neurosciences, tumor tissue characterization, risk factor identification in epidemiology, and clinical decision support.