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Daniel Sonntag

2 papers indexed

arxivcs.CV2026-07-01

Beyond Heatmaps: Unsupervised Concept-Graph Reasoning for Interpretable Visual Explanation

Md Mohasin Hossain, Anar Amirli, Robert Leist, Md Abdul Kadir, Daniel Sonntag

Concept Bottleneck Models (CBMs) provide an intrinsically interpretable alternative to post-hoc explanations. However, existing CBMs often rely on predefined concept vocabularies or supervised annotations, lack explicit concept grounding, and summarize each concept with a single…

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arxivcs.CVcs.LG2026-06-29

TRACE: A Concept Bottleneck Model for Longitudinal 3D Glioblastoma Response Assessment

Alia Tarek, Hamsa Saberr, Hamza Elghonemy, Youssef Afify, Tamer Basha, Omair Shahzad Bhatti, et al.

Longitudinal glioblastoma response assessment requires comparing subtle tumor changes across MRI time points using structured clinical criteria such as RANO. However, most deep learning methods predict response labels directly from imaging features, which limits clinical inspecti…

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