重新思考医学图像分类中的基础模型：基于MedMNIST基准研究
Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST
AI总结：
本研究通过MedMNIST基准测试，评估多种基础模型在医学图像分类中的表现，采用端到端训练和线性探测，揭示预训练模型的迁移潜力，并提供模型选择见解。
摘要：
基础模型因其对下游任务的高适应性和泛化能力，在医学图像分析中被广泛采用。随着发布的基础模型数量不断增加，模型选择已成为一个重要问题。在本工作中，我们通过在MedMNIST数据集上进行基准研究，探讨基础模型在医学图像分类任务中的能力。具体而言，我们采用了从卷积到基于Transformer的各种基础模型，并对所有分类任务实施了端到端训练和线性探测。结果表明，这些预训练模型在迁移至医学图像分类时展现出显著潜力。我们进一步进行了不同图像尺寸和不同训练数据规模的实验。通过分析所有结果，我们提供了关于该主题的初步但有用的见解和结论。
英文摘要：
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.
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。
