拥抱海量医学数据
Embracing Massive Medical Data
AI总结：
针对海量医学影像持续到来导致的训练困难与灾难性遗忘，本文提出依据数据独特性和预测不确定性筛选重要样本的在线学习方法，提升数据效率，并使多器官和肿瘤分割Dice分数提高15%。
摘要：
随着扫描数量不断增加、类别持续扩展以及来源日益多样，海量医学数据变得可获得，主流训练范式——即让AI在固定、有限的数据集上进行多轮训练——面临重大挑战。首先，由于新的扫描、来源和类别会不断到来，一次性在此类海量数据上训练AI是不切实际的。其次，在新的扫描、来源和类别上持续训练AI可能导致灾难性遗忘，即AI在学习新数据时遗忘旧数据，反之亦然。为应对这两个挑战，我们提出一种在线学习方法，使AI能够从海量医学数据中训练。不同于反复在随机选择的数据样本上训练AI，我们的方法依据数据独特性和预测不确定性，识别对当前AI模型最重要的样本，然后在这些选择性数据样本上训练AI。与主流训练范式相比，我们的方法不仅通过支持在连续数据流上训练提高了数据效率，还通过选择性地在原本可能被遗忘的重要数据样本上训练AI，缓解了灾难性遗忘，并在多器官和肿瘤分割的Dice分数上取得15%的提升。代码可在https://github.com/MrGiovanni/OnlineLearning获得。
英文摘要：
As massive medical data become available with an increasing number of scans, expanding classes, and varying sources, prevalent training paradigms -- where AI is trained with multiple passes over fixed, finite datasets -- face significant challenges. First, training AI all at once on such massive data is impractical as new scans/sources/classes continuously arrive. Second, training AI continuously on new scans/sources/classes can lead to catastrophic forgetting, where AI forgets old data as it learns new data, and vice versa. To address these two challenges, we propose an online learning method that enables training AI from massive medical data. Instead of repeatedly training AI on randomly selected data samples, our method identifies the most significant samples for the current AI model based on their data uniqueness and prediction uncertainty, then trains the AI on these selective data samples. Compared with prevalent training paradigms, our method not only improves data efficiency by enabling training on continual data streams, but also mitigates catastrophic forgetting by selectively training AI on significant data samples that might otherwise be forgotten, outperforming by 15% in Dice score for multi-organ and tumor segmentation. The code is available at https://github.com/MrGiovanni/OnlineLearning
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。
