Performance uncertainty in medical image analysis: a large-scale investigation of confidence intervals
This paper investigates how to measure the uncertainty in the performance of AI models used in medical imaging. It focuses on confidence intervals, which help understand how reliable the reported performance of these models is.
Content & Liability Disclaimer
This article and its accompanying video are automated summaries derived from the original research paper by Unknown authors. The original research was conducted solely by the paper's authors; PDFdigest did not conduct any of the research and makes no claims of ownership over the underlying scientific work.
The video narration is generated by artificial intelligence and references the paper's authors for attribution. The video is not narrated by any of the paper's authors. This content may contain inaccuracies, omissions, or misinterpretations of the original research. First-person language (e.g., "we found", "our results") reflects the original authors' voice, not PDFdigest's. Always read the original paper for accurate, verified information before making any decisions based on this content.
This content is provided "as is" without any warranties, express or implied. Simulated systems OÜ, its officers, directors, employees, and agents shall not be liable for any direct, indirect, incidental, special, consequential, or punitive damages arising from your use of, reliance on, or access to this content, including but not limited to errors, omissions, or misinterpretations of the original research. This disclaimer applies to the fullest extent permitted by applicable law.
- 1 Concentration inequalities are a type of inequalities that aim at describing how much a random variable concentrates around a typical value (e.g. its mean or median) .
- 2 We place ourselves in the same setting as before, but we remove the hypothesis that the variance is known.
- 3 The null hypothesis is H 0 : E (D) = 0.
- 4 The null hypothesis is H 0 : E (X) = E (Y ).
Introduction
Despite this rapid growth, there is still little translation from the literature into routine clinical practice. One hindrance to translation is the lack of uncertainty quantification of performance estimates in research papers .
Conversely, methods producing too wide intervals are underconfident as they do not manage to locate the true value with enough precision.
Despite their importance, there are very few papers on CIs in medical imaging AI and their scope remains narrow, typically considering only a small set of models and performance metrics.
To the best of our knowledge, only a limited number of studies have examined CIs in ML for medical imaging.
Nevertheless, there are metrics (in our case HD and HD95) which remain discrete because they can only take values from a limited set (1, √ 2, √ 3, etc.).
Research Question
Concentration inequalities are a type of inequalities that aim at describing how much a random variable concentrates around a typical value (e.g. its mean or median) . To investigate this, we conducted Kolmogorov-Smirnov tests.
We place ourselves in the same setting as before, but we remove the hypothesis that the variance is known.
The hypothesis made here is that there exists a transformation function g such that, denoting ϕ = g(θ) and φ = g( θ), and τ the constant (not estimated) standard error of ϕ, φ-ϕ τ ∼ N (-z 0 σ ϕ , σ 2 ϕ ), where z 0 is called bias constant and σ.
Methodology
However, estimating uncertainty through CIs is not a trivial task. Different CI methods exist and the choice of an inappropriate method can lead to either overconfidence or underconfidence in the estimated model performance (Figure 1 ).
Study Design
A good confidence interval method should therefore balance these two aspects by accurately identifying the range of plausible values for the performance estimate while providing an informative assessment of its precision.
In other words, a CI method has two key characteristics: reliability and precision.
However, their analysis was limited to two performance metrics, two tasks and one model.
How PDFdigest Helps You Understand Research
Instant Paper Analysis
Get structured summaries and key findings from dense PDFs in seconds.
Visual Explanations
Turn complex methods, figures, and results into clearer visual breakdowns.
AI-Powered Q&A
Ask focused questions and get answers grounded in the paper.
Results & Findings
Over the past decade, there has been an exponential increase in the number of papers on machine learning (ML) for medical imaging . As a result, these estimates should be interpreted together with measures of uncertainty.
- Over the past decade, there has been an exponential increase in the number of papers on machine learning (ML) for medical imaging .
- As a result, these estimates should be interpreted together with measures of uncertainty.
- Confidence intervals (CIs) are arguably the most common way to provide a measure of the uncertainty of the reported performance estimate.
- Food and Drug Administration (FDA) requires companies to report confidence intervals along with performance estimates when submitting Artificial Intelligence (AI)-enabled medical devices for regulatory approval .
- In particular, methods producing too narrow intervals often lead to overconfidence, as the proposed interval might miss the true performance of the model.
Practical Applications
Precision means that the CI width is as small as possible. In such a case, the CI is imprecise (a tighter interval could likely have been obtained).
However, most metrics can take such a large range of possible values because images contain a large number of pixels or voxels, that they can, in practice, be seen as continuous.
However, due to prohibitive computational cost, we could only do this for some of the performance metrics.
With small sample sizes, these outliers may be missing or overrepresented in the test data.
Confidence Intervals
This section explains how confidence intervals quantify the variability of performance estimates in medical imaging AI, detailing their properties of coverage and width, and the distinction between random intervals and their realizations.
Frequently Asked Questions
Concentration inequalities are a type of inequalities that aim at describing how much a random variable concentrates around a typical value (e.g. its mean or median) . We place ourselves in the same setting as before, but we remove the hypothesis that.
In particular, a wide range of task-and application-specific performance metrics are required and guidelines have been established to support appropriate metric selection . A wide variety of performance metrics are used in medical imaging AI, and they need to be carefully chosen.
Confidence intervals (CIs) are arguably the most common way to provide a measure of the uncertainty of the reported performance estimate. A prototypical example is segmentation where a segmentation map is produced for each individual image and compared to a reference using.
Indeed, performance estimates are computed from test sets of finite size and are therefore inherently subject to sampling variability. This is clinically important: even in large datasets, rare classes, which may correspond to uncommon but medically relevant disease types, can make precise.
However, their analysis was limited to two performance metrics, two tasks and one model. With small sample sizes, these outliers may be missing or overrepresented in the test data.
This paper investigates how to measure the uncertainty in the performance of AI models used in medical imaging. It focuses on confidence intervals, which help understand how reliable the reported performance of these models is.