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Uncertainty: Overview

Uncertainty: Overview

In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.

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Uncertainty Estimation for Tumor Prediction with Unlabeled Data.

Juyoung Yun¹, Shahira Abousamra¹, Chen Li¹

¹Stony Brook University, Department of Computer Science, USA.

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|November 18, 2024

Summary

This summary is machine-generated.

This study introduces a new learning method for digital pathology models to better estimate neural network uncertainty using unlabeled data. The approach enhances model transparency and trustworthiness by effectively utilizing available data.

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Area of Science:

Digital pathology
Machine learning
Uncertainty quantification

Background:

Estimating neural network uncertainty is vital for transparency and trustworthiness in AI models.
Digital pathology generates vast amounts of unlabeled data, presenting a challenge for model training.
Existing methods may not fully exploit unlabeled data for uncertainty estimation.

Purpose of the Study:

To develop a novel learning method for uncertainty estimation in digital pathology prediction models.
To effectively leverage large unlabeled datasets in digital pathology.
To improve the trustworthiness and transparency of AI models in this field.

Main Methods:

Proposed a novel learning method designed to exploit unlabeled data.
Applied the method to digital pathology prediction tasks.
Compared performance against baseline methods, including Monte-Carlo Dropout.

Main Results:

The proposed method demonstrated superior performance compared to existing baselines.
Analysis of uncertain regions provided insights into model behavior.
The approach enhanced the trustworthiness of the digital pathology models.

Conclusions:

The novel learning method effectively utilizes unlabeled data for uncertainty estimation in digital pathology.
This approach improves model transparency and trustworthiness.
Further inspection of model uncertainty can yield valuable insights.