在口腔状细胞癌组织病理学中使用联合学习框架对瘤透淋巴细胞进行分类
Barun Barua1, Genevieve Chyrmang1, Kangkana Bora2
1Department of Computer Science and IT, Cotton University, Guwahati, Assam, 781001, India.
在口腔状细胞癌 (OSCC) 中,自动化瘤透淋巴细胞 (TIL) 量化是至关重要的. 我们的OralTILs-ViT框架集成了细胞和组织数据,以准确,可重复的OSCC分级.
科学领域:
- 计算病理学计算病理学
- 医疗图像分析 医学图像分析
- 机器学习在瘤学中的应用
背景情况:
- 口腔状细胞癌 (OSCC) 的预后不佳,瘤透淋巴细胞 (TIL) 是关键的预后指标.
- 目前的OSCC手册TIL量化方法是主观的,导致诊断不一致.
- 现有的自动化方法缺乏细胞层次的细节和对OSCC分级至关重要的子类别.
研究的目的:
- 在OSCC中开发一个自动化框架,用于准确的TIL量化和分类.
- 整合细胞和组织层面的信息,以提高诊断准确度.
- 为TILs提供透子类别,与已建立的病理学分级系统保持一致.
主要方法:
- 提议OralTILs-ViT,一种新的联合表示学习框架,使用细胞和组织特征的双平行编码器.
- 推出了TILSeg-MobileViT,这是一个弱监督的细分模型,用于生成蜂密度图,减少手动注释需求.
- 机密TILs透到"中度到标记"",轻微"和"没有到非常少"的类别.
主要成果:
- OralTILs-ViT实现了高性能指标:96.37%的准确性,96.34%的精度,96.37%的回忆力,96.35%的F1得分.
- 托普西斯分析证实,拟议的方法在所有TIL透类别中排名第一.
- 双编码器方法有效地捕获了复杂的组织-细胞相互作用,以进行准确的分类.
结论:
- 拟议的OralTILs-ViT框架为OSCC.TILs分类提供了一个准确和可重复的自动化解决方案.
- 整合细胞和组织层面的信息显著优于单一模式的方法.
- 这种方法解决了手动评估和以前的自动化技术的局限性,支持更好的OSCC分级.
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