2022/10/18 by Lea Goetz, Goetz, Lea
Computer Science · Mathematics · #AI in cancer detection #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Statistical Methods and Inference #Statistical Methods in Clinical Trials
paper · pdf · doi:10.48550/arxiv.2210.09909
openalex publication_date 2022/10/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In computational histopathology algorithms now outperform humans on a range of tasks, but to date none are employed for automated diagnoses in the clinic. Before algorithms can be involved in such high-stakes decisions they need to "know when they don't know", i.e., they need to estimate their predictive uncertainty. This allows them to defer potentially erroneous predictions to a human pathologist, thus increasing their safety. Here, we evaluate the predictive performance and calibration of several uncertainty estimation methods on clinical histopathology data. We show that a distance-aware uncertainty estimation method outperforms commonly used approaches, such as Monte Carlo dropout and deep ensembles. However, we observe a drop in predictive performance and calibration on novel samples across all uncertainty estimation methods tested. We also investigate the use of uncertainty thresholding to reject out-of-distribution samples for selective prediction. We demonstrate the limitations of this approach and suggest areas for future research.