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This paper deals with the multiple annotation problem in medical applicationof cancer detection in digital images. The main assumption is that thoughimages are labeled by many experts, the number of images read by the sameexpert is not large. Thus differing with the existing work on modeling eachexpert and ground truth simultaneously, the multi annotation information isused in a soft manner. The multiple labels from different experts are used toestimate the probability of the findings to be marked as malignant. Thelearning algorithm minimizes the Kullback Leibler (KL) divergence between themodeled probabilities and desired ones constraining the model to be compact.The probabilities are modeled by logit regression and multiple instancelearning concept is used by us.  Experiments on a real-life computer aided diagnosis (CAD) problem for CXR CADlung cancer detection demonstrate that the proposed algorithm leads to similarresults as learning with a binary RVMMIL classifier or a mixture of binaryRVMMIL models per annotator. However, this model achieves a smaller complexityand is more preferable in practice.

Cancer Detection with Multiple Radiologists via Soft Multiple Instance Logistic Regression and $L_1$ Regularization