A computer-implemented method for cancer detection and quantification comprising: receiving a medical image through a communications interface of a computing device over a data network; analyzing the medical image, with a processor of the computing device, to determine a first subset of contours in the medical image satisfying one or more criterion; analyzing, with the processor, one or more geometric attributes and one or more contrast attributes of contours included in the first subset of contours to identify a second subset of contours based upon contours satisfying one or more predetermined geometric and contrast attributes; selecting, with the processor, a third subset of contours from the second subset of contours that corresponds to potential calcifications, the third subset of contours selected based on contours within the second subset satisfying first calcification criteria; ranking, with the processor, contours included in the third subset of contours based on a selection metric, the selection metric accounting for a combination of contrast and intensity; grouping, with the processor, contours included in the third subset of contours into nested structures; selecting, with the processor, calcifications from the nested structures satisfying second calcification criteria; grouping, with the processor, the selected calcifications into clusters based on one or more of neighboring calcifications and a spatial cluster scale; classifying, with the processor, the clusters as benign or possible cancer by performing one or more of: a regression analysis on calcifications within the clusters, edge detection, a density analysis of the clusters, and a circularity analysis of the clusters; and scoring, with the processor, the clusters using an analytic function of geometric and contrast properties of the calcifications within each cluster, and spatial arrangements of the calcifications within each cluster. receiving a medical image through a communications interface of a computing device over a data network; analyzing the medical image, with a processor of the computing device, to determine a first subset of contours in the medical image satisfying one or more criterion; analyzing, with the processor, one or more geometric attributes and one or more contrast attributes of contours included in the first subset of contours to identify a second subset of contours based upon contours satisfying one or more predetermined geometric and contrast attributes; selecting, with the processor, a third subset of contours from the second subset of contours that corresponds to potential calcifications, the third subset of contours selected based on contours within the second subset satisfying first calcification criteria; ranking, with the processor, contours included in the third subset of contours based on a selection metric, the selection metric accounting for a combination of contrast and intensity; grouping, with the processor, contours included in the third subset of contours into nested structures; selecting, with the processor, calcifications from the nested structures satisfying second calcification criteria; grouping, with the processor, the selected calcifications into clusters based on one or more of neighboring calcifications and a spatial cluster scale; classifying, with the processor, the clusters as benign or possible cancer by performing one or more of: a regression analysis on calcifications within the clusters, edge detection, a density analysis of the clusters, and a circularity analysis of the clusters; and scoring, with the processor, the clusters using an analytic function of geometric and contrast properties of the calcifications within each cluster, and spatial arrangements of the calcifications within each cluster. <EOS>