  We propose a framework for localization and classification of masses in
breast ultrasound (BUS) images. We have experimentally found that training
convolutional neural network based mass detectors with large, weakly annotated
datasets presents a non-trivial problem, while overfitting may occur with those
trained with small, strongly annotated datasets. To overcome these problems, we
use a weakly annotated dataset together with a smaller strongly annotated
dataset in a hybrid manner. We propose a systematic weakly and semi-supervised
training scenario with appropriate training loss selection. Experimental
results show that the proposed method can successfully localize and classify
masses with less annotation effort. The results trained with only 10 strongly
annotated images along with weakly annotated images were comparable to results
trained from 800 strongly annotated images, with the 95% confidence interval of
difference -3.00%--5.00%, in terms of the correct localization (CorLoc)
measure, which is the ratio of images with intersection over union with ground
truth higher than 0.5. With the same number of strongly annotated images,
additional weakly annotated images can be incorporated to give a 4.5% point
increase in CorLoc, from 80.00% to 84.50% (with 95% confidence intervals
76.00%--83.75% and 81.00%--88.00%). The effects of different algorithmic
details and varied amount of data are presented through ablative analysis.
