multiResRandomForestRegression.RdRepresents feature images as a neighborhood across scales to build a random forest prediction from an image population. A use case for this function is to predict cognition from multiple image features, e.g. from the voxelwise FA of the corpus callosum and, in parallel, voxelwise measurements of the volume of the inferior frontal gyrus.
multiResRandomForestRegression( y, x, labelmasks, rad = NA, nsamples = 10, multiResSchedule = c(0), ntrees = 500 )
| y | vector of scalar values or labels. if a factor, do classification, otherwise regression. |
|---|---|
| x | a list of lists where each list contains feature images |
| labelmasks | a list of masks where each mask defines the image space for the given list and the number of parallel predictors. more labels means more predictors. alternatively, separate masks may be used for each feature in which case this should be a list of lists. see examples. |
| rad | vector of dimensionality d define nhood radius |
| nsamples | (per subject to enter training) |
| multiResSchedule | an integer vector defining multi-res levels |
| ntrees | (for the random forest model) |
list with a random forest model, a vector identifying which rows correspond to which subjects and a prediction vector.
Pustina, D, et al. Automated segmentation of chronic stroke lesions using LINDA: Lesion Identification with Neighborhood Data Analysis, Human Brain Mapping, 2016. (related work, not identical)
mask<-makeImage( c(100,100), 0 ) mask[ 30:60, 30:60 ]<-1 mask[ 35:45, 50:60]<-2 ilist<-list() masklist<-list() inds<-1:8 yvec<-rep(0,length(inds)) scl<-0.33 # a noise parameter for ( i in inds ) { img<-antsImageClone(mask) imgb<-antsImageClone(mask) limg<-antsImageClone(mask) img[ 3:6, 3:6 ]<-rnorm(16,1)*scl*(i)+scl*mean(rnorm(1)) imgb[ 3:6, 3:6 ]<-rnorm(16,1)*scl*(i)+scl*mean(rnorm(1)) ilist[[i]]<-list(img,imgb) # two features yvec[i]<-i^2.0 # a real outcome masklist[[i]] = antsImageClone( mask ) } r=c(1,1) mr=c(2,0) featMat <- getMultiResFeatureMatrix( ilist[[1]], masklist[[1]], rad=r, , multiResSchedule=mr ) rfm <- multiResRandomForestRegression( yvec , ilist, masklist, rad=r, multiResSchedule=mr ) preds = predict( rfm, newdata=featMat ) if (FALSE) { # data: https://github.com/stnava/ANTsTutorial/tree/master/phantomData fns = Sys.glob("phantom*wmgm.jpg") ilist = imageFileNames2ImageList( fns ) masklist = list( ) flist = list( ) for ( i in 1:length(fns) ) { # 2 labels means 2 sets of side by side predictors and features at each scale locseg = kmeansSegmentation( ilist[[i]], 2 )$segmentation masklist[[ i ]] = list( locseg, locseg %>% thresholdImage(2,2), locseg ) flist[[ i ]] = list( ilist[[i]], ilist[[i]] %>% iMath("Laplacian",1), ilist[[i]] %>% iMath("Grad",1) ) } yvec = factor( rep( c(1,2), each = 4 ) ) # classification r = c( 1, 1 ) mr = c( 2, 1, 0 ) ns = 50 trn = c(1:3,6:8) ytrain = yvec[ trn ] ftrain = flist[ trn ] mtrain = masklist[ trn ] mrrfr = multiResRandomForestRegression( ytrain, ftrain, mtrain, rad=c(1,1), nsamples = ns, multiResSchedule=mr ) mypreds = rep( NA, length( fns ) ) mymode <- function(x) { ux <- unique(x) ux[which.max(tabulate(match(x, ux)))] } for ( i in 4:5 ) # test set { fmat = getMultiResFeatureMatrix( flist[[i]], masklist[[i]], rad=r, multiResSchedule=mr, nsamples = ns ) myp = predict( mrrfr, newdata=fmat ) mypreds[ i ] = mymode( myp ) # get the most frequent observation # use median or mean for continuous predictions } print("predicted") print( mypreds[-trn] ) print("ground truth") print( yvec[-trn] ) }