In this package, we assume that the each image specified true lesion location which are evaluated base on the gold standard and reader does not know such locations.
Truth
Reader mark his suspicious location with the number indicating his confidence level.
That is, the yellow triangles means that reader thinks they are lesions with his confidence level which is denoted by the inner number in the yellow triangle.
Mark
Evaluate hits
Hit
Count false alarms
FalselAlarms
Confidence Level | No. of Hits | No. of False alarms |
---|---|---|
5 = definitely present | \(H_{5}=1\) | \(F_{5}=0\) |
4 = probably present | \(H_{4}=0\) | \(F_{4}=0\) |
3 = equivocal | \(H_{3}=0\) | \(F_{3}=1\) |
2 = probably absent | \(H_{2}=0\) | \(F_{2}=0\) |
1 = questionable | \(H_{1}=0\) | \(F_{1}=2\) |
Note that this is done only for the one image, and actual trial, there are many images, so number of hits and false alarms are more large.
dat <- list(
#Confidence level.
c = c(5,4,3,2,1),
#Number of hits for each confidence level.
h = c(1,0,0,0,0),
#Number of false alarms for each confidence level.
f = c(0,0,1,0,2),
#Number of lesions
NL= 2,
#Number of images
NI= 1,
#Number of confidence level
C= 3
)
Should not run the function with this data. This data is too small to fit the model.
For multiple reader and multiple case, the notations are little troublesome, so I remain it.
By divide each images to the voxel of interest (VOI) or region of interest (ROI), we can obtain such FROC data. These design is very similar to the ordinary ROC analysis, since each ROI or VOI we only answer the dichotomous answer whether lesion exists or not.
Note that I am not doctor, so the red circles never the actual lesions, it is only need to explain and I marked the red circle blindly. So, the image is probably healthy case. I do not know.
Very very very bad :’-D, I have prurigo nodularis at all my body from head to leg, am I die ?? I want to found job, I had quited the previous job for my bad health, so please give me a job, help me..