  Medical image classification involves thresholding of labels that represent
malignancy risk levels. Usually, a task defines a single threshold, and when
developing computer-aided diagnosis tools, a single network is trained per such
threshold, e.g. as screening out healthy (very low risk) patients to leave
possibly sick ones for further analysis (low threshold), or trying to find
malignant cases among those marked as non-risk by the radiologist ("second
reading", high threshold). We propose a way to rephrase the classification
problem in a manner that yields several problems (corresponding to different
thresholds) to be solved simultaneously. This allows the use of Multiple Task
Learning (MTL) methods, significantly improving the performance of the original
classifier, by facilitating effective extraction of information from existing
data.
