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Understanding Automatic Diagnosis and Classification Processes with Data Visualization

机译：通过数据可视化了解自动诊断和分类过程

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Providing accurate diagnosis of diseases generally requires complex analyses of many clinical, biological and pathological variables. In this context, solutions based on machine learning techniques achieved relevant results in specific disease detection and classification, and can hence provide significant clinical decision support. However, such approaches suffer from the lack of proper means for interpreting the choices made by the models, especially in case of deep-learning ones. In order to improve interpretability and explainability in the process of making qualified decisions, we designed a system that allows for a partial opening of this black box by means of proper investigations on the rationale behind the decisions; this can provide improved understandings into which pre-processing steps are crucial for better performance. We tested our approach over artificial neural networks trained for automatic medical diagnosis based on high-dimensional gene expression and clinical data. Our tool analyzed the internal processes performed by the networks during the classification tasks in order to identify the most important elements involved in the training process that influence the network’s decisions.We report the results of an experimental analysis aimed at assessing the viability of the proposed approach.
机译：提供准确的疾病诊断通常需要对许多临床，生物学和病理学变量进行复杂的分析。在这种情况下，基于机器学习技术的解决方案在特定疾病的检测和分类中取得了相关的结果，因此可以提供重要的临床决策支持。但是，这种方法缺乏解释模型选择的正确方法，特别是在深度学习的情况下。为了在做出合格决策的过程中提高解释性和可解释性，我们设计了一个系统，该系统允许通过对决策背后的原理进行适当的调查来部分打开此黑匣子。这样可以更好地了解哪些预处理步骤对于提高性能至关重要。我们在经过训练的基于高维基因表达和临床数据的自动医学诊断的人工神经网络上测试了我们的方法。我们的工具分析了分类任务期间网络执行的内部过程，以识别影响网络决策的培训过程中涉及的最重要元素。我们报告了旨在评估所提出方法可行性的实验分析结果。

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