Adrian Duric

• Kjetil, Indrehus,; Duric, Adrian; Choi, Changkyu & Ramezani-Kebrya, Ali (2026). Towards Self-Explainable Document Visual Question Answering with Chain-of-Explanation Predictions. arXiv. 2605.06058. doi: https:/arxiv.org/pdf/2605.06058. Full text in Research Archive Show summary

  Document Visual Question Answering (DocVQA) requires vision-language models to reason not only about what information in a document is relevant to a question, but also where the answer is grounded on the page. Existing DocVQA models entangle question-relevant evidence and answer localization and operate largely as black boxes, offering limited means to verify how predictions depend on visual evidence. We propose CoExVQA, a self-explainable DocVQA framework with a grounded reasoning process through a chain-of-explanation design. CoExVQA first identifies question-relevant evidence, then explicitly localizes the answer region, and finally decodes the answer exclusively from the grounded region. Prediction via CoExVQA's chain-of-explanation enables direct inspection and verification of the reasoning process across modalities. Empirical results show that restricting decoding to grounded evidence achieves SotA explainable DocVQA performance on PFL-DocVQA, improving ANLS by 12% over the current explainable baselines while providing transparent and verifiable predictions.

• Duric, Adrian; Tørresen, Jim; Riegler, Michael & Hammer, Hugo Lewi (2025). Explanation Supported Learning: Improving Prediction Performance with Explainable Artificial Intelligence. IEEE International Symposium on Computer-Based Medical Systems. p. 591–598. doi: 10.1109/CBMS65348.2025.00125. Full text in Research Archive Show summary

  When artificial intelligence (AI) and machine learning (ML) models are applied in healthcare, the ability to understand and explain model decisions is an important aspect. Methods in the field of explainable AI (XAI) have been developed to create explanations for such decisions, which provides transparency and trust to the prediction model. However, the use of XAI-based explanations as added data features for the purpose of improving prediction performance remains a little explored topic. Our proposed Explanation Supported Learning (XSL) framework can improve classification performance for ML models used in medical imaging systems, while also providing a new understanding of how medical images are processed by deep learning (DL) models. The XSL framework consists of novel methods to achieve knowledge transfer from one or several teacher models to a student model. The novelty lies in using explanations from the teacher models, obtained from XAI techniques, as added features when training the student model. This approach enables flexible knowledge transfer between models of different architecture types. We further demonstrate how the XSL framework can be used as a new metric for measuring the quality of the explanations provided by XAI methods. The achievement of increased performance in this framework requires that the chosen XAI technique contains useful information based on the learned understanding of the input data by the teacher models. By testing XSL on the HyperKvasir gastrointestinal image dataset, we achieved significant increases in most of the measured classification metrics, and exceeded most benchmark scores of the HyperKvasir paper. Our code is available on GitHub.

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