对比多种模型在医学图像分类中的表现，为选型提供实证参考。
Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST
- 横比卷积与Transformer类预训练模型在医学图像上的表现。
- 线性探测和端到端训练均显示模型具备强迁移能力。
- 适合作为医学图像分析的基线研究或模型选型参考。
基础模型在医学图像分析中广泛应用，因其对下游任务具有高适应性和泛化能力。随着基础模型数量增多，模型选择成为关键问题。本文通过在MedMNIST数据集上开展基准测试，系统评估了从卷积到基于Transformer的各种基础模型在医学图像分类任务中的表现。实验涵盖端到端训练与线性探测两种方式，并考察不同图像尺寸及训练数据量下的性能变化。结果表明，这些预训练模型在医学图像分类任务中展现出显著潜力。通过对所有实验结果的分析，我们提供了关于该领域的初步但有价值的洞察与结论。
原文摘要 · Abstract (English)
Foundation models are widely employed in medical image analysis, due to their high adaptability and generalizability for downstream tasks. With the increasing number of foundation models being released, model selection has become an important issue. In this work, we study the capabilities of foundation models in medical image classification tasks by conducting a benchmark study on the MedMNIST dataset. Specifically, we adopt various foundation models ranging from convolutional to Transformer-based models and implement both end-to-end training and linear probing for all classification tasks. The results demonstrate the significant potential of these pre-trained models when transferred for medical image classification. We further conduct experiments with different image sizes and various sizes of training data. By analyzing all the results, we provide preliminary, yet useful insights and conclusions on this topic.
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