A machine learning based quantification system for automated diagnosis of lumbar spondylolisthesis on spinal X-rays.

The automated diagnosis of lumbar spondylolisthesis lacks standardized criteria and the diagnostic of lumbar spondylolisthesis itself inherently relies on the subjective judgment of experts, resulting in a lack of standardized criteria. The objective of this study is to develop a novel, fully automated diagnostic system for lumbar spondylolisthesis. A two-stage system was developed, consisting of a Mask R-CNN with Prime Sample Attention (PISA) for vertebral segmentation in the first stage and a Light Gradient Boosting Machine (LGBM) for spondylolisthesis diagnosis in the second stage. The training data was developed by a total of 936 X-ray images including neutral, extension, and flexion lateral radiographs retrospectively gathered from 312 patients diagnosed with lumbar spondylolisthesis between January 2021 and March 2022. From a model perspective, there were a total of 4680 vertebrae, of which 1056 (22.6 %) were spondylolisthesis and the rest were normal. The Bbox mAP50, Bbox mAP75, Segm mAP50, and Segm mAP75 of Mask R-CNN with PISA were 0.9852, 0.9179, 0.9741, and 0.8957, respectively. The Accuracy, AUC, Recall, Precision, and F1-score of LGBM were 0.9660, 0.9843, 0.9020, 0.9020, and 0.9020, respectively. This study presents a robust system for the diagnosis of lumbar spondylolisthesis, providing accurate detection, classification, and localization of lumbar spondylolisthesis.

Fourier feature decorrelation based sample attention for dense crowd localization.

Crowd localization, which prevails to extract the independent individual features, plays an significant role in critical analysis for crowd scene. Dense trivial features of individual targets are frequently susceptible to interference from complex background features, which makes it difficult to obtain satisfactory predictions for individual targets. Aiming at this issue, a Fourier feature decorrelation based sample attention is proposed for dense crowd localization. The correlation between features are decoupled in the Fourier transform domain, which induces the model to focus more on the true correlation between individual target features and labels. From the perspective of Fourier feature correlation between samples, independence test statistic optimization with cross-covariance operator is developed for feature decorrelation within the sample attention framework. The sample attention with global weight learning is iteratively optimized through matching the prediction loss, which can induce model partial out the spurious correlation between target-irrelevant features and labels. Experimental results show that the method proposed in this paper outperforms the current advanced crowd location methods on public dense crowd datasets.