自监督模型在有丝分裂图像分类中不如传统端到端训练有效。
Is Self-Supervision Enough? Benchmarking Foundation Models Against End-to-End Training for Mitotic Figure Classification
- 用线性探测评估五个公开自监督模型的性能
- 端到端训练的ResNet50在所有数据量下均更优
- 自监督模型未展现更强的跨域泛化能力
基础模型（FMs）通过大规模无标签数据预训练，提取输入图像块的语义特征向量，可搭配简单分类器实现少样本学习并增强领域鲁棒性。本文在两个公开的有丝分裂图像数据集上，对比了五种公开基础模型的线性探测表现与ImageNet预训练的ResNet50及端到端训练基线模型。结果表明，无论数据量多少，端到端训练的基线模型始终优于所有基于基础模型的分类器。此外，基础模型在应对领域偏移时并未表现出更强的鲁棒性，说明其在该任务中并非更优选择。
原文摘要 · Abstract (English)
Foundation models (FMs), i.e., models trained on a vast amount of typically unlabeled data, have become popular and available recently for the domain of histopathology. The key idea is to extract semantically rich vectors from any input patch, allowing for the use of simple subsequent classification networks potentially reducing the required amounts of labeled data, and increasing domain robustness. In this work, we investigate to which degree this also holds for mitotic figure classification. Utilizing two popular public mitotic figure datasets, we compared linear probing of five publicly available FMs against models trained on ImageNet and a simple ResNet50 end-to-end-trained baseline. We found that the end-to-end-trained baseline outperformed all FM-based classifiers, regardless of the amount of data provided. Additionally, we did not observe the FM-based classifiers to be more robust against domain shifts, rendering both of the above assumptions incorrect.
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