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    LISE: A Logic-Based Interactive Similarity Explainer for Clusters of RDF Data by Simona Colucci, Francesco Maria Donini, Verdiana Schena, Floriano Scioscia, Eugenio Di Sciascio

    Published 2025-01-01
    “…This paper introduces LISE (Logic-based Interactive Similarity Explainer), an integrated and model-agnostic framework designed to generate explainable, human-readable insights into clusters of RDF resources. LISE combines four core components: (i) a machine learning module leveraging vector embeddings and k-means clustering; (ii) a logic-based reasoning component that computes the common semantic features of clustered items via an optimized Least Common Subsumer (LCS); (iii) a Natural Language Generation (NLG) module that verbalizes these features into structured and human-readable explanations; and (iv) an interactive user feedback loop that captures user perception of explanation relevance to iteratively enhance embedding quality and cluster interpretability. …”
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    Real-time morphological and dosimetric adaptation in nasopharyngeal carcinoma radiotherapy: insights from autosegmented fractional fan-beam CT by Xiao-li Yu, Jiang Hu, Yu-xian Yang, Guang-yu Wang, Xin Yang, Wen-chao Diao, Lu Liu, Xiao-bo Jiang, Chen-di Xu, Liu-wen Lin, Le-cheng Jia, Hua Li, Yan-fei Liu, Ying Sun, Guan-qun Zhou

    Published 2025-04-01
    “…Abstract Background To quantify morphological and dosimetric variations in nasopharyngeal carcinoma (NPC) radiotherapy via autosegmented fan-beam computed tomography (FBCT) and to inform decision-making regarding appropriate objectives and optimal timing for adaptive radiotherapy (ART). …”
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