Exploiting monotonicity constraints for active learning in ordinal classification

We consider ordinal classification and instance ranking problems where each attribute is known to have an increasing or decreasing relation with the class label or rank. For example, it stands to reason that the number of query terms occurring in a document has a positive influence on its relevance to the query. We aim to exploit such monotonicity constraints by using labeled attribute vectors to draw conclusions about the class labels of order related unlabeled ones. Assuming we have a pool of unlabeled attribute vectors, and an oracle that can be queried for class labels, the central problem is to choose a query point whose label is expected to provide the most information. We evaluate different query strategies by comparing the number of inferred labels after some limited number of queries, as well as by comparing the prediction errors of models trained on the points whose labels have been determined so far. We present an efficient algorithm to determine the query point preferred by the well-known active learning strategy generalized binary search. This algorithm can be applied to binary classification on incomplete matrix orders. For non-binary classification, we propose to include attribute vectors in the training set whose class labels have not been uniquely determined yet. We perform experiments on artificial and real data

Exploiting Monotonicity Constraints in Active Learning for Ordinal Classification

We consider ordinal classication and instance ranking problems where each attribute is known to have an increasing or decreasing relation with the class label or rank. For example, it stands to reason that the number of query terms occurring in a document has a positive in uence on its relevance to the query. We aim to exploit such monotonicity constraints by using labeled attribute vectors to draw conclusions about the class labels of order related unlabeled ones. Assuming we have a pool of unlabeled attribute vectors, and an oracle that can be queried for class labels, the central problem is to choose a query point whose label is expected to provide the most information. We evaluate dierent query strategies by comparing the number of inferred labels after some limited number of queries, as well as by comparing the prediction errors of models trained on the points whose labels have been determined so far. We present an ecient algorithm to determine the query point preferred by the well-known active learning strategy generalized binary search. This algorithm can be applied to binary classication on incomplete matrix orders. For non-binary classication, we propose to include attribute vectors in the training set whose class labels have not been uniquely determined yet. We perform experiments on articial and real data

Exploiting Monotonicity Constraints in Active Learning for Ordinal Classification

We consider ordinal classication and instance ranking problems where each attribute is known to have an increasing or decreasing relation with the class label or rank. For example, it stands to reason that the number of query terms occurring in a document has a positive in uence on its relevance to the query. We aim to exploit such monotonicity constraints by using labeled attribute vectors to draw conclusions about the class labels of order related unlabeled ones. Assuming we have a pool of unlabeled attribute vectors, and an oracle that can be queried for class labels, the central problem is to choose a query point whose label is expected to provide the most information. We evaluate dierent query strategies by comparing the number of inferred labels after some limited number of queries, as well as by comparing the prediction errors of models trained on the points whose labels have been determined so far. We present an ecient algorithm to determine the query point preferred by the well-known active learning strategy generalized binary search. This algorithm can be applied to binary classication on incomplete matrix orders. For non-binary classication, we propose to include attribute vectors in the training set whose class labels have not been uniquely determined yet. We perform experiments on articial and real data

Comparison of detection of trauma-related injuries using combined "all-in-one" fused images and conventionally reconstructed images in acute trauma CT

OBJECTIVES To compare the accuracy of lesion detection of trauma-related injuries using combined "all-in-one" fused (AIO) and conventionally reconstructed images (CR) in acute trauma CT. METHODS In this retrospective study, trauma CT of 66 patients (median age 47 years, range 18-96 years; 20 female (30.3%)) were read using AIO and CR. Images were independently reviewed by 4 blinded radiologists (two residents and two consultants) for trauma-related injuries in 22 regions. Sub-analyses were performed to analyze the influence of experience (residents vs. consultants) and body region (chest, abdomen, skeletal structures) on lesion detection. Paired t-test was used to compare the accuracy of lesion detection. The effect size was calculated (Cohen's d). Linear mixed-effects model with patients as the fixed effect and random forest models were used to investigate the effect of experience, reconstruction/image processing, and body region on lesion detection. RESULTS Reading time of residents was significantly faster using AIO (AIO: 266 ± 72 s, CR: 318 ± 113 s; p < 0.001; d = 0.46) while no significant difference was observed in the accuracy of lesion detection (AIO: 93.5 ± 6.0%, CR: 94.6 ± 6.0% p = 0.092; d =  - 0.21). Reading time of consultants showed no significant difference (AIO: 283 ± 82 s, CR: 274 ± 95 s; p = 0.067; d = 0.16). Accuracy was significantly higher using CR; however, the difference and effect size were very small (AIO 95.1 ± 4.9%, CR: 97.3 ± 3.7%, p = 0.002; d =  - 0.39). The linear mixed-effects model showed only minor effect of image processing/reconstruction for lesion detection. CONCLUSIONS Residents at the emergency department might benefit from faster reading time without sacrificing lesion detection rate using AIO for trauma CT. KEY POINTS • Image fusion techniques decrease the reading time of acute trauma CT without sacrificing diagnostic accuracy