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.
Abstract (translated)
喉癌的成像研究缺乏标准化的数据集，以支持可重复的深度学习（DL）模型开发。我们提出了LaryngealCT，这是一个由六个来自癌症影像档案馆（TCIA）的集合汇总而成的基准数据集，包含1,029份计算机断层扫描（CT）图像。使用弱监督参数搜索框架并经临床专家验证后，提取了涵盖喉部的统一1毫米等轴距感兴趣体积。 三维DL架构（包括3D CNN、ResNet18、50和101、DenseNet121）在两类任务上进行了基准测试：早期阶段（Tis, T1, T2）与晚期阶段（T3, T4），以及T4与非T4的分类任务。在两个任务中，3D CNN（AUC-0.881，F1-macro-0.821）和ResNet18（AUC-0.892，F1-macro-0.646）分别超过了其他模型的表现。 通过使用带有甲状软骨叠加的三维GradCAM进行模型解释性评估，非T4病例显示了更显著的周围软骨关注区域，而T4预测则显示出焦点激活。通过开源数据、预训练模型和集成可解释工具，LaryngealCT为支持喉癌临床决策的人工智能研究提供了可重复的基础。
URL
https://arxiv.org/abs/2510.11047
