Automated prostate cancer detection and diagnosis using a boosted ensemble of bagging ensemble models

A non-transitory computer-readable storage medium for use in conjunction with a computer system, the computer-readable storage medium configured to store program instructions that, when executed by the computer system, causes the computer system to perform one or more operations comprising:

• receiving medical-imaging data associated with a pelvic region of an individual;
• computing features associated with voxels corresponding to a prostate of the individual based at least in part on the medical-imaging data, wherein, for a given voxel, the features comprise:
• intensity features, texture features, and a spatial feature corresponding to a distance from a peripheral zone of the prostate to a transition zone of the prostate;
• determining, on a voxel-by-voxel basis, cancer predictions for the voxels based at least in part on the computed features and a pretrained predictive model, wherein the pretrained predictive model comprises a boosted parallel random forests (BPRF) model, wherein the pretrained BPRF model comprises a boosted ensemble of bagging ensemble models, and wherein a given bagging ensemble model comprises an ensemble of random forests models;
• and
• providing feedback based at least in part on the cancer predictions for the voxels, wherein, for the given voxel, the feedback comprises:
• a cancer prediction and a location.

A method for providing feedback, comprising:

• by a computer system:
• receiving medical-imaging data associated with a pelvic region of an individual;
• computing features associated with voxels corresponding to a prostate of the individual based at least in part on the medical-imaging data, wherein, for a given voxel, the features comprise:
• intensity features, texture features, and a spatial feature corresponding to a distance from a peripheral zone of the prostate to a transition zone of the prostate;
• determining, on a voxel-by-voxel basis, cancer predictions for the voxels based at least in part on the computed features and a pretrained predictive model, wherein the pretrained predictive model comprises a boosted parallel random forests (BPRF) model, wherein the pretrained BPRF model comprises a boosted ensemble of bagging ensemble models, and wherein a given bagging ensemble model comprises an ensemble of random forests models;
• and
• providing the feedback based at least in part on the cancer predictions for the voxels, wherein, for the given voxel, the feedback comprises:
• a cancer prediction and a location.