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We propose a method for effectively utilizing weakly annotated image data inan object detection tasks of breast ultrasound images. Given the problemsetting where a small, strongly annotated dataset and a large, weakly annotateddataset with no bounding box information are available, training an objectdetection model becomes a non-trivial problem. We suggest a controlled weightfor handling the effect of weakly annotated images in a two stage objectdetection model. We~also present a subsequent active learning scheme for safelyassigning weakly annotated images a strong annotation using the trained model.Experimental results showed a 24\% point increase in correct localization(CorLoc) measure, which is the ratio of correctly localized and classifiedimages, by assigning the properly controlled weight. Performing active learningafter a model is trained showed an additional increase in CorLoc. We tested theproposed method on the Stanford Dog datasets to assure that it can be appliedto general cases, where strong annotations are insufficient to obtainresembling results. The presented method showed that higher performance isachievable with lesser annotation effort.

Gradually Applying Weakly Supervised and Active Learning for Mass Detection in Breast Ultrasound Images