Hello this is JNPMEDI.
In medical imaging analysis, the ability to assess the presence and precise location of lesions, such as those associated with lung or breast cancer, is a critical indicator for measuring the performance of radiologists or medical AI systems.
We will take a closer look at various specialized ROC (Receiver Operating Characteristic) curves used for evaluating such performance. We will explore the concepts, characteristics, and practical applications of each type of ROC curve in detail.
1. LROC (Localization ROC) Curve
The LROC curve evaluates images that either do not contain lesions or contain at most one lesion.
The x-axis represents the proportion of non-lesion images incorrectly identified as false positives, while the y-axis represents the proportion of cases where both the presence and location of the lesion are correctly assessed.
A key feature of the LROC curve is that the area under the curve (AUC) can be calculated, enabling the quantitative comparison of model performance. It is a suitable metric for evaluating the accuracy of distinguishing clinically significant single lesions, such as those associated with breast cancer, in medical imaging.
2. FROC (Free-response ROC) Curve
The FROC curve evaluates images without restrictions on the number of lesions included (free-response).
The x-axis represents the average number of false-positives per image. For example, if 100 images are evaluated and there are 250 instances where non-lesion areas are mistakenly marked as lesions, the average number of false positives per image would be 250 divided by 100, resulting in 2.5.
The y-axis represents the proportion of images in which the evaluator accurately assesses both the presence and location of lesions.
The x-axis values of the FROC curve are not ratios (range: 0 to 1) but positive numbers representing the average number of false positives, meaning the range is not limited. As a result, the x-axis range varies for each curve, making direct comparisons between curves more challenging.
3. AFROC (Alternative FROC) Curve
The AFROC curve was proposed as an alternative to address the limitations of the FROC curve.
In this curve, the x-axis is defined as the fraction of images containing false positives, and the y-axis represents the fraction of cases where both the presence and location of lesions are accurately assessed. Both axes range from 0 to 1.
With these characteristics, the area under the curve (AUC) can be calculated, making it easier to quantitatively compare different curves.
4. JAFROC (Jackknife Alternative FROC) Curve
The JAFROC curve is a variant of the AFROC curve, where 'J' stands for the Jackknife Resampling method.
The Jackknife Resampling method is used to calculate the variance required for statistical testing in studies where multiple readers evaluate multiple medical images, known as Multi-Reader Multi-Case (MRMC) studies.
5. wAFROC (Weighted Alternative FROC) Curve
The wAFROC curve was introduced to address the issue of images with a large number of lesions having an overly significant impact on the results.
In wAFROC, weights are assigned based on the number of lesions per image, reducing the influence of specific images and ensuring that all images contribute equally to the results. For example, if an image contains three lesions, each lesion can be assigned a weight of 1/3.
This approach controls the disproportionate impact of images with many lesions, enabling stable performance evaluation across datasets with varying numbers of lesions.
-
Through this article, we explored how specialized ROC curves used in medical imaging analysis contribute to the performance evaluation and advancement of medical AI technology.
As AI continues to establish itself as a core driver of innovation in the healthcare industry, ongoing efforts to ensure safety and reliability through such evaluation tools remain essential.
We will continue to provide practical insights that can be applied in clinical settings.
Thank you.
๐ References
โ๏ธ Original Source: https://tea-tasting-statisticians.github.io/posts/Special_ROC_Curves/
โ๏ธ AFROC, FROC Curve Image Source: Metz CE. Receiver operating characteristic analysis: a tool for the quantitative evaluation of observer performance and imaging systems. J Am Coll Radiol. 2006 Jun;3(6):413-22. doi: 10.1016/j.jacr.2006.02.021. PMID: 17412096.
โ๏ธ Concept Reference: https://dpc10ster.github.io/RJafrocFrocBook/
Hello this is JNPMEDI.
In medical imaging analysis, the ability to assess the presence and precise location of lesions, such as those associated with lung or breast cancer, is a critical indicator for measuring the performance of radiologists or medical AI systems.
We will take a closer look at various specialized ROC (Receiver Operating Characteristic) curves used for evaluating such performance. We will explore the concepts, characteristics, and practical applications of each type of ROC curve in detail.
1. LROC (Localization ROC) Curve
The LROC curve evaluates images that either do not contain lesions or contain at most one lesion.
The x-axis represents the proportion of non-lesion images incorrectly identified as false positives, while the y-axis represents the proportion of cases where both the presence and location of the lesion are correctly assessed.
A key feature of the LROC curve is that the area under the curve (AUC) can be calculated, enabling the quantitative comparison of model performance. It is a suitable metric for evaluating the accuracy of distinguishing clinically significant single lesions, such as those associated with breast cancer, in medical imaging.
2. FROC (Free-response ROC) Curve
The FROC curve evaluates images without restrictions on the number of lesions included (free-response).
The x-axis represents the average number of false-positives per image. For example, if 100 images are evaluated and there are 250 instances where non-lesion areas are mistakenly marked as lesions, the average number of false positives per image would be 250 divided by 100, resulting in 2.5.
The y-axis represents the proportion of images in which the evaluator accurately assesses both the presence and location of lesions.
The x-axis values of the FROC curve are not ratios (range: 0 to 1) but positive numbers representing the average number of false positives, meaning the range is not limited. As a result, the x-axis range varies for each curve, making direct comparisons between curves more challenging.
3. AFROC (Alternative FROC) Curve
The AFROC curve was proposed as an alternative to address the limitations of the FROC curve.
In this curve, the x-axis is defined as the fraction of images containing false positives, and the y-axis represents the fraction of cases where both the presence and location of lesions are accurately assessed. Both axes range from 0 to 1.
With these characteristics, the area under the curve (AUC) can be calculated, making it easier to quantitatively compare different curves.
4. JAFROC (Jackknife Alternative FROC) Curve
The JAFROC curve is a variant of the AFROC curve, where 'J' stands for the Jackknife Resampling method.
The Jackknife Resampling method is used to calculate the variance required for statistical testing in studies where multiple readers evaluate multiple medical images, known as Multi-Reader Multi-Case (MRMC) studies.
5. wAFROC (Weighted Alternative FROC) Curve
The wAFROC curve was introduced to address the issue of images with a large number of lesions having an overly significant impact on the results.
In wAFROC, weights are assigned based on the number of lesions per image, reducing the influence of specific images and ensuring that all images contribute equally to the results. For example, if an image contains three lesions, each lesion can be assigned a weight of 1/3.
This approach controls the disproportionate impact of images with many lesions, enabling stable performance evaluation across datasets with varying numbers of lesions.
-
Through this article, we explored how specialized ROC curves used in medical imaging analysis contribute to the performance evaluation and advancement of medical AI technology.
As AI continues to establish itself as a core driver of innovation in the healthcare industry, ongoing efforts to ensure safety and reliability through such evaluation tools remain essential.
We will continue to provide practical insights that can be applied in clinical settings.
Thank you.
๐ References
โ๏ธ Original Source: https://tea-tasting-statisticians.github.io/posts/Special_ROC_Curves/
โ๏ธ AFROC, FROC Curve Image Source: Metz CE. Receiver operating characteristic analysis: a tool for the quantitative evaluation of observer performance and imaging systems. J Am Coll Radiol. 2006 Jun;3(6):413-22. doi: 10.1016/j.jacr.2006.02.021. PMID: 17412096.
โ๏ธ Concept Reference: https://dpc10ster.github.io/RJafrocFrocBook/