Automated disease diagnosis using medical image analysis relies on deep learning, often requiring large labeled datasets for supervised model training. Diseases like Acute Myeloid Leukemia (AML) pose challenges due to scarce and costly annotations on a single-cell level. Multiple Instance Learning (MIL) addresses weakly labeled scenarios but necessitates powerful encoders typically trained with labeled data. In this study, we explore Self-Supervised Learning (SSL) as a pre-training approach for MIL-based AML subtype classification from blood smears, removing the need for labeled data during encoder training. We investigate the three state-of-the-art SSL methods SimCLR, SwAV, and DINO, and compare their performance against supervised pre-training. Our findings show that SSL-pretrained encoders achieve comparable performance, showcasing the potential of SSL in MIL. This breakthrough offers a cost-effective and data-efficient solution, propelling the field of AI-based disease diagnosis.

翻译：利用医学图像分析进行自动化疾病诊断依赖于深度学习，通常需要大规模标注数据集来训练监督模型。诸如急性髓系白血病（Acute Myeloid Leukemia, AML）等疾病，由于单细胞层面的标注稀缺且成本高昂，带来了严峻挑战。多示例学习（Multiple Instance Learning, MIL）能处理弱标签场景，但需要有强大的编码器，而这些编码器通常需要依赖标注数据进行训练。在本研究中，我们探索将自监督学习（Self-Supervised Learning，SSL）作为MIL方法中用于血涂片AML亚型分类的预训练策略，从而消除编码器训练过程中对标注数据的需求。我们研究了三种最先进的SSL方法——SimCLR、SwAV和DINO，并将其性能与有监督预训练进行了比较。研究结果表明，采用SSL预训练的编码器能够达到与有监督预训练相当的性能，展现了SSL在MIL中的应用潜力。这一突破提供了经济高效且数据利用率高的解决方案，推动了基于人工智能的疾病诊断领域的发展。