Evaluation of predictive deep learning (DL) models beyond conventional performance metrics has become increasingly important for applications in sensitive environments like healthcare. Such models might have the capability to encode and analyse large sets of data but they often lack comprehensive interpretability methods, preventing clinical trust in predictive outcomes. Quantifying uncertainty of a prediction is one way to provide such interpretability and promote trust. However, relatively little attention has been paid to how to include such requirements into the training of the model. In this paper we: (i) quantify the data (aleatoric) and model (epistemic) uncertainty of a DL model for Cardiac Resynchronisation Therapy response prediction from cardiac magnetic resonance images, and (ii) propose and perform a preliminary investigation of an uncertainty-aware loss function that can be used to retrain an existing DL image-based classification model to encourage confidence in correct predictions and reduce confidence in incorrect predictions. Our initial results are promising, showing a significant increase in the (epistemic) confidence of true positive predictions, with some evidence of a reduction in false negative confidence.

翻译：常规性能衡量标准以外的预测性深层次学习(DL)模型的评价对于保健等敏感环境中的应用越来越重要,这些模型可能有能力对大量数据集进行编码和分析,但它们往往缺乏全面的可解释性方法,从而防止对预测结果的临床信任。量化预测的不确定性是提供这种可解释性和促进信任的一种方法。然而,对于如何将这类要求纳入模型培训的问题,注意相对较少。在本文件中,我们:(一) 量化卡迪亚克恢复同步模型的数据(代言)和模型(流行性)不确定性,从心脏磁共振图像中预测对治疗性反应的预测,以及(二) 提议和进行对不确定性-认知损失功能的初步调查,用于重新培养现有的基于DL图像的分类模型,以鼓励对正确预测的信心和减少对错误预测的信心。我们的初步结果很有希望,表明对真实正面预测的(流行性)信心有显著提高,并有一些证据表明虚伪负信心的减少。