数字病理学中面向切片级癌症亚型分类的基础模型
Foundation Models for Slide-level Cancer Subtyping in Digital Pathology
AI总结：
本文比较了不同预训练策略的特征提取器在MIL框架下对WSI癌症亚型分类的性能，证明基础模型在六种皮肤癌亚型预测上优于ImageNet预训练模型。
摘要：
自ImageNet数据集出现以来，预训练与微调方法因其使ImageNet预训练模型能够学习多种视觉特征的能力，已在计算机视觉领域得到广泛采用。然而，当将这些模型适配到数字病理学等特定领域时，由于领域之间存在显著差距，会面临重大挑战。为解决这一局限，基础模型（FM）已在大规模领域内数据集上进行训练，以学习组织病理学图像的复杂特征。在癌症诊断中，全切片图像（WSI）预测对患者预后至关重要，并且已采用多实例学习（MIL）来处理WSI的十亿像素级尺寸。由于MIL框架依赖于图块级特征聚合，本工作旨在比较在MIL框架下，不同预训练策略开发的各种特征提取器在WSI癌症亚型分类中的性能。结果表明，基础模型在预测六种皮肤癌亚型方面能够超越ImageNet预训练模型。
英文摘要：
Since the emergence of the ImageNet dataset, the pretraining and fine-tuning approach has become widely adopted in computer vision due to the ability of ImageNet-pretrained models to learn a wide variety of visual features. However, a significant challenge arises when adapting these models to domain-specific fields, such as digital pathology, due to substantial gaps between domains. To address this limitation, foundation models (FM) have been trained on large-scale in-domain datasets to learn the intricate features of histopathology images. In cancer diagnosis, whole-slide image (WSI) prediction is essential for patient prognosis, and multiple instance learning (MIL) has been implemented to handle the giga-pixel size of WSI. As MIL frameworks rely on patch-level feature aggregation, this work aims to compare the performance of various feature extractors developed under different pretraining strategies for cancer subtyping on WSI under a MIL framework. Results demonstrate the ability of foundation models to surpass ImageNet-pretrained models for the prediction of six skin cancer subtypes
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。
