{"ID":2856136,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.11047","arxiv_id":"2510.11047","title":"Benchmarking Deep Learning Models for Laryngeal Cancer Staging Using the LaryngealCT Dataset","abstract":"Laryngeal cancer imaging research lacks standardised datasets to enable reproducible deep learning (DL) model development. We present LaryngealCT, a curated benchmark of 1,029 computed tomography (CT) scans aggregated from six collections from The Cancer Imaging Archive (TCIA). Uniform 1 mm isotropic volumes of interest encompassing the larynx were extracted using a weakly supervised parameter search framework validated by clinical experts. 3D DL architectures (3D CNN, ResNet18,50,101, DenseNet121) were benchmarked on (i) early (Tis,T1,T2) vs. advanced (T3,T4) and (ii) T4 vs. non-T4 classification tasks. 3D CNN (AUC-0.881, F1-macro-0.821) and ResNet18 (AUC-0.892, F1-macro-0.646) respectively outperformed the other models in the two tasks. Model explainability assessed using 3D GradCAMs with thyroid cartilage overlays revealed greater peri-cartilage attention in non-T4 cases and focal activations in T4 predictions. Through open-source data, pretrained models, and integrated explainability tools, LaryngealCT offers a reproducible foundation for AI-driven research to support clinical decisions in laryngeal oncology.","short_abstract":"Laryngeal cancer imaging research lacks standardised datasets to enable reproducible deep learning (DL) model development. We present LaryngealCT, a curated benchmark of 1,029 computed tomography (CT) scans aggregated from six collections from The Cancer Imaging Archive (TCIA). Uniform 1 mm isotropic volumes of interes...","url_abs":"https://arxiv.org/abs/2510.11047","url_pdf":"https://arxiv.org/pdf/2510.11047v1","authors":"[\"Nivea Roy\",\"Son Tran\",\"Atul Sajjanhar\",\"K. Devaraja\",\"Prakashini Koteshwara\",\"Yong Xiang\",\"Divya Rao\"]","published":"2025-10-13T06:25:19Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Convolutional Neural Network\"]","has_code":false}