Journal of Imaging, Volume 12, Issue 2
2026 February - 38 articles
Cover Story: This cover illustrates a unified framework for benchmarking explainability in 3D deep learning. The visual composition symbolically represents cross-domain evaluation across volumetric medical imaging, voxelized CAD objects, and point-cloud data. By evaluating correctness, completeness, and compactness, the work highlights domain-dependent explanation behaviors and structured trade-offs between causal alignment and spatial compactness. The visual synthesis reflects the complementary strengths of intrinsic and post hoc explainability approaches, emphasizing the importance of multi-metric evaluation for reliable 3D XAI systems. View this paper
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J. Imaging - ISSN 2313-433X