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.

通过自监督学习方法在血涂片中为基于 MIL 的 AML 亚型分类提供无标签数据的预训练编码器，实现与有监督预训练相媲美的性能，为 AI 基础疾病诊断领域提供了一种经济高效的解决方案。

自监督多实例学习用于急性髓系白血病分类

Self-Supervised Multiple Instance Learning for Acute Myeloid Leukemia  Classification

The success of deep learning models deployed in the real world depends
critically on their ability to generalize well across diverse data domains.
Here, we address a fundamental challenge with selective classification during
automated diagnosis with domain-shifted medical images. In this scenario,
models must learn to avoid making predictions when label confidence is low,
especially when tested with samples far removed from the training set
(covariate shift). Such uncertain cases are typically referred to the clinician
for further analysis and evaluation. Yet, we show that even state-of-the-art
domain generalization approaches fail severely during referral when tested on
medical images acquired from a different demographic or using a different
technology. We examine two benchmark diagnostic medical imaging datasets
exhibiting strong covariate shifts: i) diabetic retinopathy prediction with
retinal fundus images and ii) multilabel disease prediction with chest X-ray
images. We show that predictive uncertainty estimates do not generalize well
under covariate shifts leading to non-monotonic referral curves, and severe
drops in performance (up to 50%) at high referral rates (>70%). We evaluate
novel combinations of robust generalization and post hoc referral approaches,
that rescue these failures and achieve significant performance improvements,
typically >10%, over baseline methods. Our study identifies a critical
challenge with referral in domain-shifted medical images and finds key
applications in reliable, automated disease diagnosis.

对领域转变的医学图像中的可靠自动疾病诊断中的转诊存在重要挑战，并找到了关键应用。