Deployments of artificial intelligence in medical diagnostics mandate not just accuracy and efficacy but also trust, emphasizing the need for explainability in machine decisions. The recent trend in automated medical image diagnostics leans towards the deployment of Transformer-based architectures, credited to their impressive capabilities. Since the self-attention feature of transformers contributes towards identifying crucial regions during the classification process, they enhance the trustability of the methods. However, the complex intricacies of these attention mechanisms may fall short of effectively pinpointing the regions of interest directly influencing AI decisions. Our research endeavors to innovate a unique attention block that underscores the correlation between 'regions' rather than 'pixels'. To address this challenge, we introduce an innovative system grounded in prototype learning, featuring an advanced self-attention mechanism that goes beyond conventional ad-hoc visual explanation techniques by offering comprehensible visual insights. A combined quantitative and qualitative methodological approach was used to demonstrate the effectiveness of the proposed method on the large-scale NIH chest X-ray dataset. Experimental results showed that our proposed method offers a promising direction for explainability, which can lead to the development of more trustable systems, which can facilitate easier and rapid adoption of such technology into routine clinics. The code is available at www.github.com/NUBagcilab/r2r_proto.

翻译：人工智能在医疗诊断中的应用不仅要求准确性和有效性，还要求可信度，这凸显了机器决策可解释性的必要性。当前自动化医学图像诊断的趋势倾向于部署基于Transformer的架构，这得益于其强大的能力。由于Transformer的自注意力机制有助于在分类过程中识别关键区域，因此增强了方法的可信度。然而，这些注意力机制的复杂细节可能无法有效定位直接影响AI决策的感兴趣区域。我们的研究致力于创新一种独特的注意力模块，强调“区域”而非“像素”之间的相关性。为了解决这一挑战，我们引入了一种基于原型学习的创新系统，该系统采用先进的自我注意力机制，通过提供可理解的视觉洞察，超越了传统的临时性视觉解释技术。采用定量与定性相结合的方法，在大型NIH胸部X光数据集上证明了所提出方法的有效性。实验结果表明，我们的方法为可解释性提供了一个有前景的方向，这有助于开发更可信的系统，从而促进此类技术在日常临床中的便捷快速应用。代码可在www.github.com/NUBagcilab/r2r_proto获取。