Deep learning has the potential to augment several clinically useful aspects of the radiologist's workflow such as medical imaging interpretation. However, the translation of deep learning algorithms into clinical practice has been hindered by relative lack of transparency in these algorithms compared to more traditional statistical methods. Specifically, common deep learning models lack intuitive and rigorous methods of conveying prediction confidence in a calibrated manner, which ultimately restricts widespread use of these "black box" systems for critical decision-making. Furthermore, numerous demonstrations of algorithmic bias in clinical machine learning have caused considerable hesitancy towards the deployment of these models for clinical application. To this end, we explore how conformal predictions can complement existing deep learning approaches by providing an intuitive way of expressing model uncertainty to facilitate greater transparency to clinical users. In this paper, we conduct field interviews with radiologists to assess potential use-cases of conformal predictors. Using insights collected from these interviews, we devise two use-cases and empirically evaluate several conformal methods on a dermatology photography dataset for skin lesion classification. Additionally, we show how group conformal predictors are more adaptive to differences between patient skin tones for malignant skin lesions. We find our conformal predictors to be a promising and generally applicable approach to increasing clinical usability and trustworthiness -- hopefully facilitating better modes of collaboration between medical AI tools and their clinical users.
翻译:深度学习有可能增加放射学家工作流程的若干临床有用的方面,如医学成像解释。然而,将这些深层次学习算法转化为临床实践,由于这些算法相对缺乏透明度,与较传统的统计方法相比,这些算法相对缺乏透明度,因此受到阻碍。具体地说,共同深层次学习模型缺乏直观和严格的方法,无法以校准的方式表达预测信心,最终限制了这些“黑盒”系统在关键决策方面的广泛使用。此外,临床机学习中的许多算法偏差示范,导致对将这些模型用于临床应用产生了相当大的偏差。为此,我们探索了符合性的预测如何通过提供直观的方式表达模型不确定性,促进临床用户更大的透明度,从而补充现有的深层次学习方法。在本文件中,我们与放射学家进行实地访谈,以评估可能使用哪些“黑盒”系统进行关键决策。我们利用这些访谈所收集的洞察,设计了两种使用案例,并用经验评估了用于皮肤病变分类的临床摄影数据集中的若干符合的方法。我们展示了小组的合规性预测,如何使我们更能适应性地对病人的皮肤和皮肤上的信任进行更可靠的预测。