Leonardo Mangia
Training lesion detectors from noisy annotations: an empirical study in mammography.
Rel. Fabrizio Lamberti, Lia Morra. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Biomedica, 2019
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Abstract
Machine learning algorithms need carefully-annotated datasets, these are not always available for medical images. When it is possible the annotations, such in mammography, need the presence of experts and it is much more costly than general object detection task. Moreover, the opinion of the experts may not be unanimous. As a consequence, in many cases may happen that it is not feasible having scrupulously-annotated datasets. One alternative solution is to use the annotations that are already available in clinics or hospitals, which may not be particularly made for research. These annotations are usually bigger than the actual size of the lesions.
This enlargement can be considered as noise, which is been modeled and injected in a publicly available dataset
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