Task-specific regularization loss towards model calibration for reliable
  lung cancer detection
Mehar Prateek Kalra Mansi Singhal Rohan Raju Dhanakashirur
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.