Vision transformer supported Kolmogorov-Arnold networks for survival prediction in lung cancer
Bildiriler > Tam Metin Bildiri
Yazarlar: Mert Gökpınar, Yasin Almalioglu, Mustafa Taha Kocyigit, Tamer Kahveci, Derya DEMİR, Kayhan Başak, Mehmet Turan
Proceedings of the 16th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, , , Uluslararası, Tam Metin Bildiri, ss 1-6
Özet
Lung cancer is one of the most common and deadly cancers worldwide. Accurate survival prediction is critical for guiding treatment, yet existing deep learning approaches often struggle with capturing the complexity of histological and tabular features and fusing them effectively. We address these challenges by introducing a novel Kolmogorov-Arnold Network for tabular and fusion tasks, combined with advanced vision models for histology image processing. Experiments show that our method achieves superior survival prediction accuracy compared to unimodal predictors. Furthermore, it provides explainable predictions, as 10 of the top 20 genes identified as most influential are known to play roles in cancer survival and progression.