Benchmarking common uncertainty estimation methods with histopathological images under domain shift and label noise.

In the past years, deep learning has seen an increase in usage in the domain of histopathological applications. However, while these approaches have shown great potential, in high-risk environments deep learning models need to be able to judge their uncertainty and be able to reject inputs when there is a significant chance of misclassification. In this work, we conduct a rigorous evaluation of the most commonly used uncertainty and robustness methods for the classification of Whole Slide Images, with a focus on the task of selective classification, where the model should reject the classification in situations in which it is uncertain.

Uncertainty Estimation in Medical Image Classification: Systematic Review.

Background: Deep neural networks are showing impressive results in different medical image classification tasks. However, for real-world applications, there is a need to estimate the network's uncertainty together with its prediction.

Objective: In this review, we investigate in what form uncertainty estimation has been applied to the task of medical image classification.

Remote sensing of oligotrophic waters: model divergence at low chlorophyll concentrations.

The performance of the OC2 Sea-viewing Wide Field-of-view Sensor (SeaWiFS) algorithm based on 490- and 555-nm water-leaving radiances at low chlorophyll contents is compared with those of semianalytical models and a Monte Carlo radiative transfer model. We introduce our model, which uses two particle phase functions and scattering coefficient parameterizations to achieve a backscattering ratio that varies with chlorophyll concentration. We discuss the various parameterizations and compare them with existent measurements.