Lung cancer is one of the significant causes of cancer-related deaths globally. Early detection and treatment improve the chances of survival. Traditionally CT scans have been used to extract the most significant lung infection information and diagnose cancer. This process is carried out manually by an expert radiologist. The imbalance in the radiologists-to-population ratio in a country like India implies significant work pressure on them and thus raises the need to automate a few of their responsibilities. The tendency of modern-day Deep Neural networks to make overconfident mistakes limit their usage to detect cancer. In this paper, we propose a new task-specific loss function to calibrate the neural network to reduce the risk of overconfident mistakes. We use the state-of-the-art Multi-class Difference in Confidence and Accuracy (MDCA) loss in conjunction with the proposed task-specific loss function to achieve the same. We also integrate post-hoc calibration by performing temperature scaling on top of the train-time calibrated model. We demonstrate 5.98% improvement in the Expected Calibration Error (ECE) and a 17.9% improvement in Maximum Calibration Error (MCE) as compared to the best-performing SOTA algorithm.

我们提出了一种新的任务特定损失函数来校准神经网络，以减少过于自信的错误，并结合多分类置信度和准确性（MDCA）损失函数，通过进行温度缩放的后校准，实现期望校准错误（ECE）的提升5.98%和最大校准错误（MCE）的提升17.9%，相较于最佳性能SOTA算法。

面向模型校准的任务特定正则化损失用于可靠的肺癌检测