Detecting Bone Lesions in X-Ray Under Diverse Acquisition Conditions

The diagnosis of primary bone tumors is challenging, as the initial complaints are often non-specific. Early detection of bone cancer is crucial for a favorable prognosis. Incidentally, lesions may be found on radiographs obtained for other reasons. However, these early indications are often missed. In this work, we propose an automatic algorithm to detect bone lesions in conventional radiographs to facilitate early diagnosis. Detecting lesions in such radiographs is challenging: first, the prevalence of bone cancer is very low; any method must show high precision to avoid a prohibitive number of false alarms. Second, radiographs taken in health maintenance organizations (HMOs) or emergency departments (EDs) suffer from inherent diversity due to different X-ray machines, technicians and imaging protocols. This diversity poses a major challenge to any automatic analysis method. We propose to train an off-the-shelf object detection algorithm to detect lesions in radiographs. The novelty of our approach stems from a dedicated preprocessing stage that directly addresses the diversity of the data. The preprocessing consists of self-supervised region-of-interest detection using vision transformer (ViT), and a foreground-based histogram equalization for contrast enhancement to relevant regions only. We evaluate our method via a retrospective study that analyzes bone tumors on radiographs acquired from January 2003 to December 2018 under diverse acquisition protocols. Our method obtains 82.43% sensitivity at 1.5% false-positive rate and surpasses existing preprocessing methods. For lesion detection, our method achieves 82.5% accuracy and an IoU of 0.69. The proposed preprocessing method enables to effectively cope with the inherent diversity of radiographs acquired in HMOs and EDs.

Probabilistic index maps for modeling natural signals

One of the major problems in modeling natural signals is that signals with very similar structure may locally have completely different measurements, e.g., images taken under different illumination conditions, or the speech signal captured in different environments. While there have been many successful attempts to address these problems in application-specific settings, we believe that underlying a large set of problems in signal representation is a representational deficiency of intensity-derived local measurements that are the basis of most efficient models. We argue that interesting structure in signals is better captured when the signal is de- fined as a matrix whose entries are discrete indices to a separate palette of possible measurements. In order to model the variability in signal structure, we define a signal class not by a single index map, but by a probability distribution over the index maps, which can be estimated from the data, and which we call probabilistic index maps. The existing algorithm can be adapted to work with this representation. Furthermore, the probabilistic index map representation leads to algorithms with computational costs proportional to either the size of the palette or the log of the size of the palette, making the cost of significantly increased invariance to non-structural changes quite bearable. We illustrate the benefits of the probabilistic index map representation in several applications in computer vision and speech processing.