ProtoAL：面向医学影像的基于原型的可解释深度主动学习
ProtoAL: Interpretable Deep Active Learning with prototypes for medical imaging
AI总结：
本文提出ProtoAL方法，将基于原型的可解释深度学习模型集成到深度主动学习框架中，以解决医学影像AI-CAD的可解释性不足和数据需求高的问题，并在Messidor数据集上仅用76.54%标注数据取得0.79的AUPRC。
摘要：
深度学习算法在医学影像领域的应用是一个突出的研究方向，具有推动基于人工智能的计算机辅助诊断（AI-CAD）解决方案发展的巨大潜力。然而，当前解决方案由于缺乏可解释性特征和对数据的高需求而面临挑战，这促使了近期为解决这些问题所做的努力。在本研究中，我们提出了ProtoAL方法，将可解释的深度学习模型集成到深度主动学习（DAL）框架中。该方法旨在通过聚焦医学影像场景并利用基于原型的固有可解释模型来同时应对这两项挑战。我们在Messidor数据集上对ProtoAL进行了评估，在仅使用76.54%的可用标注数据的情况下，实现了0.79的精确率-召回率曲线下面积。这些能力可以增强深度学习模型在医学领域的实际可用性，为领域专家提供信任校准的手段，并为常见的数据稀缺场景下的学习提供合适的解决方案。
英文摘要：
The adoption of Deep Learning algorithms in the medical imaging field is a prominent area of research, with high potential for advancing AI-based Computer-aided diagnosis (AI-CAD) solutions. However, current solutions face challenges due to a lack of interpretability features and high data demands, prompting recent efforts to address these issues. In this study, we propose the ProtoAL method, where we integrate an interpretable DL model into the Deep Active Learning (DAL) framework. This approach aims to address both challenges by focusing on the medical imaging context and utilizing an inherently interpretable model based on prototypes. We evaluated ProtoAL on the Messidor dataset, achieving an area under the precision-recall curve of 0.79 while utilizing only 76.54\% of the available labeled data. These capabilities can enhances the practical usability of a DL model in the medical field, providing a means of trust calibration in domain experts and a suitable solution for learning in the data scarcity context often found.
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