Snuffy：高效的全切片图像分类器
Snuffy: Efficient Whole Slide Image Classifier
AI总结：
提出Snuffy架构，一种基于稀疏transformers的MIL池化方法，通过有限预训练和持续少样本预训练解决WSI分类的计算挑战，并在CAMELYON16和TCGA肺癌数据集上取得卓越准确率。
摘要：
数字病理学中采用多实例学习（MIL）的全切片图像（WSI）分类面临着重大的计算挑战。当前方法大多依赖广泛的自监督学习（SSL）以获得令人满意的性能，这需要漫长的训练周期和大量的计算资源。同时，由于从自然图像到WSI的领域偏移，不进行预训练会影响性能。我们提出了Snuffy架构，这是一种基于稀疏transformers的新型MIL池化方法，它通过有限的预训练减轻了性能损失，并支持持续的少样本预训练，作为一个有竞争力的选项。我们的稀疏模式专为病理学量身定制，并在理论上被证明是一个通用近似器，具有迄今为止关于稀疏transformers层数的最严格概率尖锐界限。我们在CAMELYON16和TCGA肺癌数据集上证明了Snuffy的有效性，实现了卓越的WSI和patch级别的准确率。代码可在https://github.com/jafarinia/snuffy获取。
英文摘要：
Whole Slide Image (WSI) classification with multiple instance learning (MIL) in digital pathology faces significant computational challenges. Current methods mostly rely on extensive self-supervised learning (SSL) for satisfactory performance, requiring long training periods and considerable computational resources. At the same time, no pre-training affects performance due to domain shifts from natural images to WSIs. We introduce Snuffy architecture, a novel MIL-pooling method based on sparse transformers that mitigates performance loss with limited pre-training and enables continual few-shot pre-training as a competitive option. Our sparsity pattern is tailored for pathology and is theoretically proven to be a universal approximator with the tightest probabilistic sharp bound on the number of layers for sparse transformers, to date. We demonstrate Snuffy's effectiveness on CAMELYON16 and TCGA Lung cancer datasets, achieving superior WSI and patch-level accuracies. The code is available on https://github.com/jafarinia/snuffy.
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。
