用24万张皮肤图像训练小型皮肤领域基础模型，提升临床可用性。
Towards Scalable Foundation Models for Digital Dermatology
- 基于24万张皮肤图像，用自监督学习训练小型领域基础模型。
- 在12个下游任务中表现超越通用模型，接近50倍大模型性能。
- 模型轻量适配临床场景，代码与模型开源供研究使用。
数字皮肤科对准确且公平的AI模型需求日益增长，但面临多样化高质量标注数据稀缺的挑战。本文探索特定领域基础模型在解决该问题上的潜力，利用自监督学习（SSL）技术，在超过24万张来自公开与私有来源的皮肤科图像上预训练模型。研究对比了多种SSL方法，并评估所得基础模型在12个下游任务中的表现，涵盖在ImageNet上预训练的通用模型及MONET等先进模型。不同于以往研究，本文侧重于开发适用于资源受限临床环境的小型模型，便于广泛部署。结果表明，本研究所训模型不仅优于通用模型，且在临床诊断任务中接近50倍更大的模型性能。为推动该方向研究，本文公开发布训练代码与基础模型，助力皮肤科应用发展。
原文摘要 · Abstract (English)
The growing demand for accurate and equitable AI models in digital dermatology faces a significant challenge: the lack of diverse, high-quality labeled data. In this work, we investigate the potential of domain-specific foundation models for dermatology in addressing this challenge. We utilize self-supervised learning (SSL) techniques to pre-train models on a dataset of over 240,000 dermatological images from public and private collections. Our study considers several SSL methods and compares the resulting foundation models against domain-agnostic models like those pre-trained on ImageNet and state-of-the-art models such as MONET across 12 downstream tasks. Unlike previous research, we emphasize the development of smaller models that are more suitable for resource-limited clinical settings, facilitating easier adaptation to a broad range of use cases. Results show that models pre-trained in this work not only outperform general-purpose models but also approach the performance of models 50 times larger on clinically relevant diagnostic tasks. To promote further research in this direction, we publicly release both the training code and the foundation models, which can benefit clinicians in dermatological applications.
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