  In this work we study the impact of noise on the training of object detection
networks for the medical domain, and how it can be mitigated by improving the
training procedure. Annotating large medical datasets for training data-hungry
deep learning models is expensive and time consuming. Leveraging information
that is already collected in clinical practice, in the form of text reports,
bookmarks or lesion measurements would substantially reduce this cost.
Obtaining precise lesion bounding boxes through automatic mining procedures,
however, is difficult. We provide here a quantitative evaluation of the effect
of bounding box coordinate noise on the performance of Faster R-CNN object
detection networks for breast mass detection. Varying degrees of noise are
simulated by randomly modifying the bounding boxes: in our experiments,
bounding boxes could be enlarged up to six times the original size. The noise
is injected in the CBIS-DDSM collection, a well curated public mammography
dataset for which accurate lesion location is available. We show how, due to an
imperfect matching between the ground truth and the network bounding box
proposals, the noise is propagated during training and reduces the ability of
the network to correctly classify lesions from background. When using the
standard Intersection over Union criterion, the area under the FROC curve
decreases by up to 9%. A novel matching criterion is proposed to improve
tolerance to noise.
