The opacity of Artificial Intelligence (AI) systems is a major impediment to their deployment. Explainable AI (XAI) methods that automatically generate counterfactual explanations for AI decisions can increase users' trust in AI systems. Coherence is an essential property of explanations but is not yet addressed sufficiently by existing XAI methods. We design a novel optimizationbased approach to generate coherent counterfactual explanations, which is applicable to numerical, categorical, and mixed data. We demonstrate the approach in a realistic setting and assess its efficacy in a humangrounded evaluation. Results suggest that our approach produces explanations that are perceived as coherent as well as suitable to explain the factual situation.

The rising expectations of customers have considerably contributed to the need for automated approaches supporting employees in online customer service. Since automated approaches still struggle to meet the challenge to fully grasp the semantics of texts, hybrid approaches combining the complementary strengths of human and artificial intelligence show great potential for assisting employees. While research in Case-Based Reasoning (CBR) already provides well-established approaches, they do not fully exploit the potential of CBR as hybrid intelligence. Against this background, we follow a design-oriented approach and develop an adapted textual CBR cycle that integrates employees’ feedback on semantic similarity, which is collected during the Reuse phase, into the Retrieve phase by means of long-term feedback methods from information retrieval. Using a real-world data set, we demonstrate the practical applicability and evaluate our approach regarding performance in online customer service. Our novel approach surpasses human-based, machine-based, and hybrid approaches in terms of effectiveness due to a refined retrieval of semantically similar customer problems. It is further favorable regarding efficiency, reducing the average time required to solve a customer problem.

We introduce the Fast Algorithm for Motion Correction (FALCON) software, which allows correction of both rigid and nonlinear motion artifacts in dynamic whole-body (WB) images, irrespective of the PET/CT system or the tracer. Methods: Motion was corrected using affine alignment followed by a diffeomorphic approach to account for nonrigid deformations. In both steps, images were registered using multiscale image alignment. Moreover, the frames suited to successful motion correction were automatically estimated by calculating the initial normalized cross-correlation metric between the reference frame and the other moving frames. To evaluate motion correction performance, WB dynamic image sequences from 3 different PET/CT systems (Biograph mCT, Biograph Vision 600, and uEXPLORER) using 6 different tracers ( 18 F-FDG, 18 F-fluciclovine, 68 Ga-PSMA, 68 Ga-DOTA-TATE, 11 C-Pittsburgh compound B, and 82 Rb) were considered. Motion correction accuracy was assessed using 4 different measures: change in volume mismatch between individual WB image volumes to assess gross body motion, change in displacement of a large organ (liver dome) within the torso due to respiration, change in intensity in small tumor nodules due to motion blur, and constancy of activity concentration levels. Results: Motion correction decreased gross body motion artifacts and reduced volume mismatch across dynamic frames by about 50%. Moreover, large-organ motion correction was assessed on the basis of correction of liver dome motion, which was removed entirely in about 70% of all cases. Motion correction also improved tumor intensity, resulting in an average increase in tumor SUVs by 15%. Large deformations seen in gated cardiac 82 Rb images were managed without leading to anomalous distortions or substantial intensity changes in the resulting images. Finally, the constancy of activity concentration levels was reasonably preserved (,2% change) in large organs before and after motion correction. Conclusion: FALCON allows fast and accurate correction of rigid and nonrigid WB motion artifacts while being insensitive to scanner hardware or tracer distribution, making it applicable to a wide range of PET imaging scenarios.

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