ContrastDiagnosis: Enhancing Interpretability in Lung Nodule Diagnosis
  Using Contrastive Learning
Chenglong Wang Yinqiao Yi Yida Wang Chengxiu Zhang Yun Liu Kensaku Mori Mei Yuan Guang Yang
With the ongoing development of deep learning, an increasing number of AI
models have surpassed the performance levels of human clinical practitioners.
However, the prevalence of AI diagnostic products in actual clinical practice
remains significantly lower than desired. One crucial reason for this gap is
the so-called `black box' nature of AI models. Clinicians' distrust of black
box models has directly hindered the clinical deployment of AI products. To
address this challenge, we propose ContrastDiagnosis, a straightforward yet
effective interpretable diagnosis framework. This framework is designed to
introduce inherent transparency and provide extensive post-hoc explainability
for deep learning model, making them more suitable for clinical medical
diagnosis. ContrastDiagnosis incorporates a contrastive learning mechanism to
provide a case-based reasoning diagnostic rationale, enhancing the model's
transparency and also offers post-hoc interpretability by highlighting similar
areas. High diagnostic accuracy was achieved with AUC of 0.977 while maintain a
high transparency and explainability.