Mitigating Diagnostic Errors in Lung Cancer Classification: A Multi-Eyes Principle to Uncertainty Quantification.

In radiology, particularly in lung cancer diagnosis, diagnostic errors and cognitive biases pose substantial challenges. These issues, including perceptual errors, interpretive mistakes, and cognitive biases such as anchoring and premature closure, are often unnoticed by experienced radiologists. To address these challenges, we propose the Multi-Eyes principle approach, which utilises multiple deep learning models to reduce bias and potentially improve diagnostic accuracy. Inspired by the Four-Eyes principle in business and cybersecurity, this methodology employs various 3D and 2D (for validation) deep learning architectures and three uncertainty quantification techniques: Monte Carlo Dropout, Deep Ensemble, and Ensemble Monte Carlo Dropout. Each model functions as an independent reviewer, similar to blind reviews. With entropy selected as the uncertainty measurement, it is averaged, followed by ensemble averaging of predictions. The effectiveness of this approach was demonstrated using the LIDC-IDRI dataset for lung cancer classification. Statistical analysis of the uncertainty's distribution reveals that with more models, uncertainty in incorrect predictions becomes more peaked and left skewed, indicating consensus on uncertainty levels. This results in accuracy and F1 score improvements, even with the best performing model, addressing overconfidence in single-model systems. These findings highlight the potential of the Multi-Eyes principle to significantly improve diagnostic performance in computer-aided diagnostic systems. Future research may explore different uncertainty quantification methods and feedback mechanisms for further advancement.