Abstract
Accurate localization of tumor regions from hematoxylin and eosin-stained whole-slide images is fundamental for translational research including spatial analysis, molecular profiling, and tissue architecture investigation. However, deep learning-based tumor detection trained within specific cancers may exhibit reduced robustness when applied across different tumor types. We investigated whether balanced training across cancers at modest scale can achieve high performance and generalize to unseen tumor types. A multi-cancer tumor localization model (MuCTaL) was trained on 79,984 non-overlapping tiles from four cancers (melanoma, hepatocellular carcinoma, colorectal cancer, and non-small cell lung cancer) using transfer learning with DenseNet169. The model achieved a tile-level ROC-AUC of 0.97 in validation data from the four training cancers, and 0.71 on an independent pancreatic ductal adenocarcinoma cohort. A scalable inference workflow was built to generate spatial tumor probability heatmaps compatible with existing digital pathology tools. Code and models are publicly available at this https URL.
Abstract (translated)
从苏木素和伊红染色的全玻片图像中准确定位肿瘤区域，对于包括空间分析、分子特征谱绘制以及组织结构研究在内的转化医学研究至关重要。然而，基于深度学习训练的肿瘤检测模型在特定癌症类型内表现良好，但应用于不同类型的肿瘤时可能表现出较低的鲁棒性。我们调查了跨不同类型癌症进行平衡训练能否以适度的数据规模达到高性能，并且泛化到未见过的肿瘤类型。 为此，我们构建了一个多癌症肿瘤定位模型（MuCTaL），该模型在四个癌症类型（黑色素瘤、肝细胞癌、结直肠癌和非小细胞肺癌）上进行了培训，使用DenseNet169进行迁移学习。训练集包含这四种癌症的79,984个不重叠的图像块。验证数据表明，在训练中的四类癌症中该模型达到了0.97的tile级ROC-AUC值；在独立的数据集中（胰腺导管腺癌），模型的性能为0.71。 我们还建立了一个可扩展的推理工作流程，生成的空间肿瘤概率热图与现有的数字病理学工具兼容。相关代码和模型可以在这里访问：[提供链接]（原文中未给出具体URL，请根据实际发布的网址替换）。
URL
https://arxiv.org/abs/2603.08844
