用基础模型提升皮肤癌亚型分类准确率
Foundation Models for Slide-level Cancer Subtyping in Digital Pathology
- 在全切片图像上用多种预训练模型对比特征提取能力
- 基础模型在六种皮肤癌亚型分类中超越ImageNet模型
- 适合病理学与医学图像分析研究者参考
自ImageNet数据集出现以来，预训练-微调范式在计算机视觉中广泛应用，因其能学习多样化的视觉特征。然而，当应用于数字病理学等特定领域时，由于领域间存在显著差异，该方法面临挑战。为此，研究者在大规模领域内数据集上训练了基础模型（FM），以学习组织病理图像的复杂特征。在癌症诊断中，全切片图像（WSI）预测对患者预后至关重要，而多实例学习（MIL）被用于处理具有吉字节级分辨率的WSI。由于MIL框架依赖于局部切片特征聚合，本文旨在比较不同预训练策略下构建的特征提取器在MIL框架下的皮肤癌亚型分类表现。结果表明，基础模型在六种皮肤癌亚型分类任务中优于ImageNet预训练模型。
原文摘要 · Abstract (English)
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
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