DfExtractDataset

Extract a dataset consisting of a subset of treatments/readers from a larger dataset

Analyzing the performance of artificial intelligence
(AI) systems/algorithms characterized by a 'search-and-report'
strategy. Historically observer performance has dealt with
measuring radiologists' performances in search tasks, e.g., searching
for lesions in medical images and reporting them, but the implicit
location information has been ignored. The implemented methods apply
to analyzing the absolute and relative performances of AI systems,
comparing AI performance to a group of human readers or optimizing the
reporting threshold of an AI system. In addition to performing historical
receiver operating receiver operating characteristic (ROC) analysis
(localization information ignored), the software also performs
free-response receiver operating characteristic (FROC)
analysis, where lesion localization information is used. A book
using the software has been published: Chakraborty DP: Observer
Performance Methods for Diagnostic Imaging - Foundations, Modeling,
and Applications with R-Based Examples, Taylor-Francis LLC; 2017:
<https://www.routledge.com/Observer-Performance-Methods-for-Diagnostic-Imaging-Foundations-Modeling/Chakraborty/p/book/9781482214840>.
Online updates to this book, which use the software, are at
<https://dpc10ster.github.io/RJafrocQuickStart/>,
<https://dpc10ster.github.io/RJafrocRocBook/> and at
<https://dpc10ster.github.io/RJafrocFrocBook/>. Supported data
collection paradigms are the ROC, FROC and the location ROC (LROC).
ROC data consists of single ratings per images, where a rating is
the perceived confidence level that the image is that of a diseased
patient. An ROC curve is a plot of true positive fraction vs. false
positive fraction. FROC data consists of a variable number (zero or
more) of mark-rating pairs per image, where a mark is the location
of a reported suspicious region and the rating is the confidence
level that it is a real lesion. LROC data consists of a rating and a
location of the most suspicious region, for every image. Four models
of observer performance, and curve-fitting software, are implemented:
the binormal model (BM), the contaminated binormal model (CBM), the
correlated contaminated binormal model (CORCBM), and the radiological
search model (RSM). Unlike the binormal model, CBM, CORCBM and RSM
predict 'proper' ROC curves that do not inappropriately cross the
chance diagonal. Additionally, RSM parameters are related to search
performance (not measured in conventional ROC analysis) and
classification performance. Search performance refers to finding
lesions, i.e., true positives, while simultaneously not finding false
positive locations. Classification performance measures the ability to
distinguish between true and false positive locations. Knowing these
separate performances allows principled optimization of reader or AI
system performance. This package supersedes Windows JAFROC (jackknife
alternative FROC) software V4.2.1,
<https://github.com/dpc10ster/WindowsJafroc>. Package functions are
organized as follows. Data file related function names are preceded
by 'Df', curve fitting functions by 'Fit', included data sets by 'dataset',
plotting functions by 'Plot', significance testing functions by 'St',
sample size related functions by 'Ss', data simulation functions by
'Simulate' and utility functions by 'Util'. Implemented are figures of
merit (FOMs) for quantifying performance and functions for visualizing
empirical or fitted operating characteristics: e.g., ROC, FROC, alternative
FROC (AFROC) and weighted AFROC (wAFROC) curves. For fully crossed study
designs significance testing of reader-averaged FOM differences between
modalities is implemented via either Dorfman-Berbaum-Metz or the
Obuchowski-Rockette methods. Also implemented is single treatment analysis,
which allows comparison of performance of a group of radiologists to a
specified value, or comparison of AI to a group of radiologists interpreting
the same cases. Crossed-modality analysis is implemented wherein there are
two crossed treatment factors and the aim is to determined performance in
each treatment factor averaged over all levels of the second factor. Sample
size estimation tools are provided for ROC and FROC studies; these use
estimates of the relevant variances from a pilot study to predict required
numbers of readers and cases in a pivotal study to achieve the desired power.
Utility and data file manipulation functions allow data to be read in any of
the currently used input formats, including Excel, and the results of the
analysis can be viewed in text or Excel output files. The methods are
illustrated with several included datasets from the author's collaborations.
This update includes improvements to the code, some as a result of
user-reported bugs and new feature requests, and others discovered during
ongoing testing and code simplification.

Dev Chakraborty

RJafroc

Artificial Intelligence Systems and Observer Performance

Peter Phillips

Xuetong Zhai

DfExtractDataset function

<dl><dt>dataset</dt>
<dd>The original dataset from which the subset is to be extracted</dd>
<dt>trts</dt>
<dd>A vector contains the indices of the treatments to be extracted. 
If this parameter is not supplied, all treatments are extracted.</dd>
<dt>rdrs</dt>
<dd>A vector contains the indices of the readers to be extracted. 
If this parameter is not supplied, all readers are extracted.</dd></dl>

Arguments

Extract a subset of treatments and readers from a dataset — DfExtractDataset

<dl>

<dt>dataset</dt>
<dd>The original dataset from which the subset is to be extracted</dd>


<dt>trts</dt>
<dd>A vector contains the indices of the treatments to be extracted. 
If this parameter is not supplied, all treatments are extracted.</dd>


<dt>rdrs</dt>
<dd>A vector contains the indices of the readers to be extracted. 
If this parameter is not supplied, all readers are extracted.</dd>

</dl>

DfExtractDataset: Extract a subset of treatments and readers from a dataset

Description

Usage

Value

Arguments

Details

Examples