BSED: Baseline Shapley-Based Explainable Detector

Explainable artificial intelligence (XAI) has witnessed significant advances in the field of object recognition, with saliency maps being used to highlight image features relevant to the predictions of learned models. Although these advances have made AI-based technology more interpretable to humans, several issues have come to light. Some approaches present explanations irrelevant to predictions, and cannot guarantee the validity of XAI (axioms). In this study, we propose the Baseline Shapley-based Explainable Detector (BSED), which extends the Shapley value to object detection, thereby enhancing the validity of interpretation. The Shapley value can attribute the prediction of a learned model to a baseline feature while satisfying the explainability axioms. The processing cost for the BSED is within the reasonable range, while the original Shapley value is prohibitively computationally expensive. Furthermore, BSED is a generalizable method that can be applied to various detectors in a model-agnostic manner, and interpret various detection targets without fine-grained parameter tuning. These strengths can enable the practical applicability of XAI. We present quantitative and qualitative comparisons with existing methods to demonstrate the superior performance of our method in terms of explanation validity. Moreover, we present some applications, such as correcting detection based on explanations from our method

Long-term safety and efficacy of alogliptin, a DPP-4 inhibitor, in patients with type 2 diabetes: a 3-year prospective, controlled, observational study (J-BRAND Registry)

Introduction Given an increasing use of dipeptidyl peptidase-4 (DPP-4) inhibitors to treat patients with type 2 diabetes mellitus in the real-world setting, we conducted a prospective observational study (Japan-based Clinical Research Network for Diabetes Registry: J-BRAND Registry) to elucidate the safety and efficacy profile of long-term usage of alogliptin.Research design and methods We registered 5969 patients from April 2012 through September 2014, who started receiving alogliptin (group A) or other classes of oral hypoglycemic agents (OHAs; group B), and were followed for 3 years at 239 sites nationwide. Safety was the primary outcome. Symptomatic hypoglycemia, pancreatitis, skin disorders of non-extrinsic origin, severe infections, and cancer were collected as major adverse events (AEs). Efficacy assessment was the secondary outcome and included changes in hemoglobin A1c (HbA1c), fasting blood glucose, fasting insulin and urinary albumin.Results Of the registered, 5150 (group A: 3395 and group B: 1755) and 5096 (3358 and 1738) were included for safety and efficacy analysis, respectively. Group A patients mostly (>90%) continued to use alogliptin. In group B, biguanides were the primary agents, while DPP-4 inhibitors were added in up to ~36% of patients. The overall incidence of AEs was similar between the two groups (42.7% vs 42.2%). Kaplan-Meier analysis revealed the incidence of cancer was significantly higher in group A than in group B (7.4% vs 4.8%, p=0.040), while no significant incidence difference was observed in the individual cancer. Multivariate Cox regression analysis revealed that the imbalanced patient distribution (more elderly patients in group A than in group B), but not alogliptin usage per se, contributed to cancer development. The incidence of other major AE categories was with no between-group difference. Between-group difference was not detected, either, in the incidence of microvascular and macrovascular complications. HbA1c and fasting glucose decreased significantly at the 0.5-year visit and nearly plateaued thereafter in both groups.Conclusions Alogliptin as a representative of DPP-4 inhibitors was safe and durably efficacious when used alone or with other OHAs for patients with type 2 diabetes in the real world setting