整张切片概念：一种用于病理图像的监督基础模型
Whole Slide Concepts: A Supervised Foundation Model For Pathological Images
AI总结：
本文提出了一种基于监督学习的多任务基础模型，用于病理图像分析，整合癌症亚型分类、风险估计和基因突变预测，且在资源消耗上优于自监督方法。
摘要：
基础模型（FMs）通过提供新的方法来分析组织病理学图像，正在改变计算病理学。然而，FMs通常需要在大型数据库上进行数周的训练，使它们的创建成为资源密集型过程。在本文中，我们提出了一种使用监督、端到端、多任务学习在切片级别标签上训练基础模型的方法。值得注意的是，这是第一个将癌症亚型分类、风险估计和基因突变预测整合到一个模型中的模型。所提出的模型在七个基准任务上优于自监督模型，而训练仅需要5%的计算资源。结果不仅表明监督训练可以在数据较少的情况下优于自监督方法，还提供了解决标注问题的解决方案，因为基于患者的标签通过常规临床流程广泛可用。此外，一个注意力模块在不同任务之间提供了一层可解释性，并用作检测未知癌症类型的肿瘤检测器。为了解决封闭数据集的问题，模型完全在公开可用的数据上进行训练。代码和模型权重可在https://github.com/FraunhoferMEVIS/MedicalMultitaskModeling上获取。
英文摘要：
Foundation models (FMs) are transforming computational pathology by offering new ways to analyze histopathology images. However, FMs typically require weeks of training on large databases, making their creation a resource-intensive process. In this paper, we present a training for foundation models from whole slide images using supervised, end-to-end, multitask learning on slide-level labels. Notably, it is the first model to incorporate cancer subtyping, risk estimation, and genetic mutation prediction into one model. The presented model outperforms self-supervised models on seven benchmark tasks while the training only required 5% of the computational resources. The results not only show that supervised training can outperform self-supervision with less data, but also offer a solution to annotation problems, as patient-based labels are widely available through routine clinical processes. Furthermore, an attention module provides a layer of explainability across different tasks and serves as a tumor detector for unseen cancer types. To address the issue of closed-source datasets, the model was fully trained on openly available data. The code and model weights are made available under https://github.com/FraunhoferMEVIS/MedicalMultitaskModeling.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
