用于血液病诊断的持续多示例学习
Continual Multiple Instance Learning for Hematologic Disease Diagnosis
AI总结：
针对多示例学习（MIL）在血液病诊断中面临的持续学习难题，提出基于实例注意力分数与均值距离的重演机制，有效避免灾难性遗忘并显著优于现有方法。
摘要：
实验室和临床的动态环境每天都有大量数据流入，需要定期更新训练好的机器学习模型以保持稳定的性能。持续学习本应有助于在不发生灾难性遗忘的情况下训练模型。然而，最先进的方法对多示例学习（MIL）无效，而MIL常用于基于单细胞的血液病诊断（例如白血病检测）。在此，我们提出了首个专门针对MIL量身定制的持续学习方法。我们的方法基于重演机制，从不同的包中选择单个实例。我们结合实例注意力分数以及与包均值和类均值向量的距离，精心选择要从前序任务的样本和实例中存储到示例集的部分，从而保持数据的多样性。使用来自白血病实验室的一个月真实世界数据，我们在类增量场景中研究了该方法的有效性，并将其与知名的持续学习方法进行了比较。结果表明，我们的方法显著优于最先进的方法，提供了首个针对MIL的持续学习方法。这使得模型能够适应随时间推移而发生偏移的数据分布，例如由疾病发生或潜在基因改变引起的变化。
英文摘要：
The dynamic environment of laboratories and clinics, with streams of data arriving on a daily basis, requires regular updates of trained machine learning models for consistent performance. Continual learning is supposed to help train models without catastrophic forgetting. However, state-of-the-art methods are ineffective for multiple instance learning (MIL), which is often used in single-cell-based hematologic disease diagnosis (e.g., leukemia detection). Here, we propose the first continual learning method tailored specifically to MIL. Our method is rehearsal-based over a selection of single instances from various bags. We use a combination of the instance attention score and distance from the bag mean and class mean vectors to carefully select which samples and instances to store in exemplary sets from previous tasks, preserving the diversity of the data. Using the real-world input of one month of data from a leukemia laboratory, we study the effectiveness of our approach in a class incremental scenario, comparing it to well-known continual learning methods. We show that our method considerably outperforms state-of-the-art methods, providing the first continual learning approach for MIL. This enables the adaptation of models to shifting data distributions over time, such as those caused by changes in disease occurrence or underlying genetic alterations.
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。
