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Segmentation is one of the most common tasks in medical imaging, but it often requires expensive ground truth for training. Weakly supervised methods cope with the lack of annotations, however, they often fall short compared to fully supervised ones. In this work, we propose to constrain the segmentation output with morphological operations, leading to an increase in the overall performance. In particular, we use top-hat and closing operations. We evaluate the method on high-resolution images from INBreast dataset and achieve an increase in F1 of approx. 0.14 and in recall of approx. 0.22 compared to the training without morphology loss.
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Thursday 8th July
H4-12 (short): Application: Radiology - 16:45 - 17:30 (UTC+2)
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