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Object detection methods are widely adopted for computer-aided diagnosisusing medical images. Anomalous findings are usually treated as objects thatare described by bounding boxes. Yet, many pathological findings, e.g., bonefractures, cannot be clearly defined by bounding boxes, owing to considerableinstance, shape and boundary ambiguities. This makes bounding box annotations,and their associated losses, highly ill-suited. In this work, we propose a newbone fracture detection method for X-ray images, based on a labor effective andflexible annotation scheme suitable for abnormal findings with no clearobject-level spatial extents or boundaries. Our method employs a simple,intuitive, and informative point-based annotation protocol to mark localizedpathology information. To address the uncertainty in the fracture scalesannotated via point(s), we convert the annotations into pixel-wise supervisionthat uses lower and upper bounds with positive, negative, and uncertainregions. A novel Window Loss is subsequently proposed to only penalize thepredictions outside of the uncertain regions. Our method has been extensivelyevaluated on 4410 pelvic X-ray images of unique patients. Experimentsdemonstrate that our method outperforms previous state-of-the-art imageclassification and object detection baselines by healthy margins, with an AUROCof 0.983 and FROC score of 89.6%.

A New Window Loss Function for Bone Fracture Detection and Localization in X-ray Images with Point-based Annotation