Antinuclear antibody (ANA) testing is a crucial method for diagnosing autoimmune disorders, including lupus, Sjögren's syndrome, and scleroderma. Despite its importance, manual ANA detection is slow, labor-intensive, and demands years of training. ANA detection is complicated by over 100 coexisting antibody types, resulting in vast fluorescent pattern combinations. Although machine learning and deep learning have enabled automation, ANA detection in real-world clinical settings presents unique challenges as it involves multi-instance, multi-label (MIML) learning. In this paper, a novel framework for ANA detection is proposed that handles the complexities of MIML tasks using unaltered microscope images without manual preprocessing. Inspired by human labeling logic, it identifies consistent ANA sub-regions and assigns aggregated labels accordingly. These steps are implemented using three task-specific components: an instance sampler, a probabilistic pseudo-label dispatcher, and self-paced weight learning rate coefficients. The instance sampler suppresses low-confidence instances by modeling pattern confidence, while the dispatcher adaptively assigns labels based on instance distinguishability. Self-paced learning adjusts training according to empirical label observations. Our framework overcomes limitations of traditional MIML methods and supports end-to-end optimization. Extensive experiments on one ANA dataset and three public medical MIML benchmarks demonstrate the superiority of our framework. On the ANA dataset, our model achieves up to +7.0% F1-Macro and +12.6% mAP gains over the best prior method, setting new state-of-the-art results. It also ranks top-2 across all key metrics on public datasets, reducing Hamming loss and one-error by up to 18.2% and 26.9%, respectively. The source code can be accessed at https://github.com/fletcherjiang/ANA-SelfPacedLearning.

翻译：抗核抗体（ANA）检测是诊断自身免疫性疾病（包括狼疮、干燥综合征和硬皮病）的关键方法。尽管其重要性，手动ANA检测速度慢、劳动强度大，且需要多年培训。ANA检测因存在100多种共存的抗体类型而变得复杂，导致荧光模式组合极其多样。虽然机器学习和深度学习已实现自动化，但在真实临床环境中的ANA检测面临独特挑战，因其涉及多示例多标签（MIML）学习。本文提出了一种新颖的ANA检测框架，该框架利用未经人工预处理的原始显微镜图像处理MIML任务的复杂性。受人类标注逻辑启发，该框架识别一致的ANA子区域并相应分配聚合标签。这些步骤通过三个任务特定组件实现：示例采样器、概率伪标签分配器和自适应步调权重学习率系数。示例采样器通过建模模式置信度来抑制低置信度示例，而分配器则根据示例可区分性自适应分配标签。自适应步调学习根据经验标签观察调整训练过程。我们的框架克服了传统MIML方法的局限性，并支持端到端优化。在一个ANA数据集和三个公共医疗MIML基准上的大量实验证明了我们框架的优越性。在ANA数据集上，我们的模型相比先前最佳方法实现了高达+7.0%的宏观F1分数和+12.6%的平均精度提升，创造了新的最先进结果。在公共数据集的所有关键指标上也均位列前二，将汉明损失和单错误率分别降低了最高18.2%和26.9%。源代码可通过https://github.com/fletcherjiang/ANA-SelfPacedLearning访问。