[Submitted on 1 Jul 2026]
Title:Beyond Heatmaps: Unsupervised Concept-Graph Reasoning for Interpretable Visual Explanation
View a PDF of the paper titled Beyond Heatmaps: Unsupervised Concept-Graph Reasoning for Interpretable Visual Explanation, by Md Mohasin Hossain (1 and 2) and 15 other authors
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Abstract: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 image-level score -- discarding spatial recurrence and inter-concept dependencies. We propose a Graph-based Concept Bottleneck Model (G-CBM), an intrinsically interpretable framework that performs unsupervised concept discovery via Non-negative Matrix Factorization (NMF) and represents the discovered concepts as nodes in a per-image concept-graph representation. G-CBM matches region-level features to these concept nodes -- providing concept grounding and capturing concept recurrence across the image -- and applies a \emph{tunable concept filtering threshold} $\tau$ to suppress weak region-level features. A Graph Attention Network (GAT) then performs concept-level reasoning by modeling nonlinear dependencies across nodes. Across ImageNet, HAM10000, PH2, and Derm7pt, G-CBM achieves an average relative AUC improvement of 3.7\% over a ResNet-50 baseline. Concept filtering frequently improves predictive performance while inducing selective concept use, achieving peak AUC of $0.96$ on PH2 with only 2 of 10 concepts and 0.92 on HAM10000 with 3.8 of 9 concepts. On dermoscopy benchmarks, G-CBM is competitive with supervised approaches requiring external annotations. Deletion/insertion analyses with random ablation controls show that the learned concept ranking faithfully reflects model predictions.
Comments: Accepted at the IJCAI-ECAI 2026 Workshop on Explainable Artificial Intelligence (XAI), Bremen, Germany. 7 pages, 4 figures
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2607.01416 [cs.CV]
(or arXiv:2607.01416v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.01416
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Md Mohasin Hossain [view email] [v1] Wed, 1 Jul 2026 19:21:39 UTC (3,352 KB)
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