结直肠癌分割：自适应增强与多分辨率集成模型
Colorectal Cancer Segmentation with Adaptive Augmentation and Multi-Resolution Ensemble Models
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中文总结 AI 辅助
提出一种基于自适应增强与多分辨率集成模型的结直肠癌全切片图像分割流程，利用密集预测Transformer和软投票集成，将F1分数从62.92提升至69.84。
中文摘要 AI 辅助
结直肠癌（CRC）是第二大致命且第三常见的癌症，也是胃肠道癌症中的主要死因。早期诊断对于该癌症的治疗和提高生存率至关重要。尽管CRC在发达地区更为常见，但其发病率在发展中地区也在上升。CRC诊断依赖于活检后的组织病理学评估。自动化深度学习算法可以显著缩短诊断时间，提高效率并支持及时的临床决策。我们提出了一种用于全切片组织病理学图像的自动化分割流程，可标记肿瘤等级1-3和正常黏膜。该流程利用具有多种编码器骨干的密集预测Transformer、重叠补丁和测试时增强。由大型语言模型引导的自适应增强策略进一步改善了训练。通过软投票集成顶级模型，并采用掩膜细化后处理步骤，包括高斯模糊、形态学闭运算和连通成分分析。在一个结直肠癌分级数据集上，我们的方法将F1分数从62.92提高到69.84。代码可在此处获取：此HTTP URL。
英文摘要
Colorectal cancer (CRC) is the second most deadly and third most common cancer, and the leading cause of death among gastrointestinal cancers. Early diagnosis is crucial for the treatment of this cancer and increasing the survival rates. Although CRC is more common in developed regions, its occurrence is also increasing in developing regions as well. CRC diagnosis relies on histopathology assessment post-biopsy. Automated deep learning algorithms can significantly reduce diagnosis time, enhancing efficiency and supporting timely clinical decisions. We present an automated segmentation pipeline for whole-slide histopathology images that labels tumor grades 1-3 and normal mucosa. It utilizes dense prediction transformers with various encoder backbones, overlapping patches, and test-time augmentation. An adaptive augmentation policy, guided by large language models, further improves training. Top models were ensembled via soft voting, and mask refining post-processing steps, Gaussian blurring, morphological closing, and connected components analysis. On a colorectal cancer grade dataset, our method improved the F1 score from 62.92 to 69.84. Code is available here: github.com/caglarmert/ICIP2025
发表机构
- Graduate School of Informatics, METU（中东技术大学信息学研究生院）
机构由 AI 辅助整理，请以论文原文为准。
