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    Feature Selection in Cancer Classification: Utilizing Explainable Artificial Intelligence to Uncover Influential Genes in Machine Learning Models by Matheus Dalmolin, Karolayne S. Azevedo, Luísa C. de Souza, Caroline B. de Farias, Martina Lichtenfels, Marcelo A. C. Fernandes

    Published 2024-12-01
    “…This study investigates the use of machine learning (ML) models combined with explainable artificial intelligence (XAI) techniques to identify the most influential genes in the classification of five recurrent cancer types in women: breast cancer (BRCA), lung adenocarcinoma (LUAD), thyroid cancer (THCA), ovarian cancer (OV), and colon adenocarcinoma (COAD). …”
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    Classification of peripheral vitreoretinal interface lesions using spectral-domain optical coherence tomography with guidance of ultrawide field imaging by Weiwei Zheng, Ying Huang, Ying Huang, Shanshan Qian, Bing Lin, Bing Lin, Shenghai Huang, Shenghai Huang

    Published 2025-02-01
    “…Of the 37 vitreoretinal tuft lesions, 32.4% were classified as category B2 and 16.2% as category C, according to peripheral OCT classification. …”
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    Evaluation of the significance of tumor stromal patterns and peri-tumoral inflammation in head and neck squamous cell carcinoma with special reference to the Yamamoto–Kohama classi... by Geet Bhuyan, Prabir Hazarika, Anju M. Rabha

    Published 2024-04-01
    “…Results: Immature SR was not observed in any of the well-differentiated squamous cell carcinoma (SCC) cases. However, one (3.7%) case of moderately differentiated SCC and two (28.6%) cases of poorly differentiated SCC showed signs of immature SR. …”
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