  Object detection methods are widely adopted for computer-aided diagnosis
using medical images. Anomalous findings are usually treated as objects that
are described by bounding boxes. Yet, many pathological findings, e.g., bone
fractures, cannot be clearly defined by bounding boxes, owing to considerable
instance, shape and boundary ambiguities. This makes bounding box annotations,
and their associated losses, highly ill-suited. In this work, we propose a new
bone fracture detection method for X-ray images, based on a labor effective and
flexible annotation scheme suitable for abnormal findings with no clear
object-level spatial extents or boundaries. Our method employs a simple,
intuitive, and informative point-based annotation protocol to mark localized
pathology information. To address the uncertainty in the fracture scales
annotated via point(s), we convert the annotations into pixel-wise supervision
that uses lower and upper bounds with positive, negative, and uncertain
regions. A novel Window Loss is subsequently proposed to only penalize the
predictions outside of the uncertain regions. Our method has been extensively
evaluated on 4410 pelvic X-ray images of unique patients. Experiments
demonstrate that our method outperforms previous state-of-the-art image
classification and object detection baselines by healthy margins, with an AUROC
of 0.983 and FROC score of 89.6%.
