Human-AI cooperative classification (HAI-CC) aims to develop hybrid intelligent systems that enhance decision-making in various high-stakes real-world scenarios by leveraging both human expertise and AI capabilities. Current HAI-CC methods primarily focus on learning-to-defer (L2D), where decisions are deferred to human experts when AI is not confident, and learning-to-complement (L2C), where AI and human experts make predictions cooperatively. However, existing research in both L2D and L2C has not effectively been explored under diverse expert knowledge to improve decision-making, particularly when constrained by the operation cost of human involvement. In this paper, we address this research gap by proposing the Coverage-constrained Learning to Defer and Complement with Specific Experts (CL2DC) method. In particular, CL2DC assesses input data before making final decisions through either AI prediction alone or by deferring to or complementing a specific human expert. Furthermore, we propose a coverage-constrained optimisation to control the cooperation cost, ensuring it approximates a target probability for AI-only selection. This approach enables an effective assessment of system performance within a specified budget. Comprehensive evaluations on both synthetic and real-world datasets demonstrate that CL2DC achieves superior performance compared to state-of-the-art HAI-CC methods.

Supply chain disruptors such as piracy and navigational obstacles like icebergs, pose a probable risk to economic development and national security. With the warming of the Arctic Sea ice due to intense changes in climatic conditions, the Arctic and the untapped natural resources that reside in the icy waters are becoming more accessible and prone to security risks. Resulting in more traffic throughout the once inaccessible region. This has made the Arctic region a geopolitical hotspot, with nations vying for the superiority of its resources. Icebergs are a navigational risk to vessels as collisions could result in delayed shipments, monetary costs due to vessel damage, and human health risks. Thus, a robust model must be developed to identify and discriminate between icebergs and vessels to aid in navigational efficacy and increase maritime domain awareness. This paper leverages a novel convolutional neural network (CNN) that employs Synthetic Aperture Radar (SAR) to differentiate between icebergs and vessels in Iceberg Alley. The 5,000-image dataset came from the Statoil and the Centre for Cold Ocean Resources Engineering (C-CORE) collaboration. The dataset consists of SAR imagery, in the HH+HV polarizations. The developed model showed promising results for iceberg and vessel classification, achieving near 90% accuracy on the prescribed dataset. Additionally, the developed model is compared to other existing model architectures to compare model efficiency. This work has a direct influence on navigational safety and transferable applicability to other emerging national security threats in the maritime domain.

Images of the kidneys using dynamic contrast-enhanced magnetic resonance renography (DCE-MRR) contains unwanted complex organ motion due to respiration. This gives rise to motion artefacts that hinder the clinical assessment of kidney function. However, due to the rapid change in contrast agent within the DCE-MR image sequence, commonly used intensity-based image registration techniques are likely to fail. While semi-automated approaches involving human experts are a possible alternative, they pose significant drawbacks including inter-observer variability, and the bottleneck introduced through manual inspection of the multiplicity of images produced during a DCE-MRR study. To address this issue, we present a novel automated, registration-free movement correction approach based on windowed and reconstruction variants of dynamic mode decomposition (WR-DMD). Our proposed method is validated on ten different healthy volunteers’ kidney DCE-MRI data sets. The results, using block-matching-block evaluation on the image sequence produced by WR-DMD, show the elimination of $$99\%$$ of mean motion magnitude when compared to the original data sets, thereby demonstrating the viability of automatic movement correction using WR-DMD.

Accurate classification of signals composed of two or more classification classes (e.g., biomedical imaging data with pathological structures) might utilize a density that takes account of the signal acquisition process. A new density based on a Gaussian point spread function (PSF) and another utilizing a phenomenological observation known as Benford's Law are presented. Histograms of filtered signals are compared with these densities. The results suggest that the Gaussian PSF-based density is somewhat better than the Benford's Law density. Both approaches provide improved fits to histograms from data convolved with a variety of different PSFs over an existing mixture formulation.