Conformal Risk Control for Pulmonary Nodule Detection
Journal:
arXiv
Published Date:
Dec 28, 2024
Abstract
Quantitative tools are increasingly appealing for decision support in
healthcare, driven by the growing capabilities of advanced AI systems. However,
understanding the predictive uncertainties surrounding a tool's output is
crucial for decision-makers to ensure reliable and transparent decisions. In
this paper, we present a case study on pulmonary nodule detection for lung
cancer screening, enhancing an advanced detection model with an uncertainty
quantification technique called conformal risk control (CRC). We demonstrate
that prediction sets with conformal guarantees are attractive measures of
predictive uncertainty in the safety-critical healthcare domain, allowing
end-users to achieve arbitrary validity by trading off false positives and
providing formal statistical guarantees on model performance. Among
ground-truth nodules annotated by at least three radiologists, our model
achieves a sensitivity that is competitive with that generally achieved by
individual radiologists, with a slight increase in false positives.
Furthermore, we illustrate the risks of using off-the-shelve prediction models
when faced with ontological uncertainty, such as when radiologists disagree on
what constitutes the ground truth on pulmonary nodules.
