Abstract
Multiple Instance Learning (MIL) is a cornerstone approach in computational pathology (CPath) for generating clinically meaningful slide-level embeddings from gigapixel tissue images. However, MIL often struggles with small, weakly supervised clinical datasets. In contrast to fields such as NLP and conventional computer vision, where transfer learning is widely used to address data scarcity, the transferability of MIL models remains poorly understood. In this study, we systematically evaluate the transfer learning capabilities of pretrained MIL models by assessing 11 models across 21 pretraining tasks for morphological and molecular subtype prediction. Our results show that pretrained MIL models, even when trained on different organs than the target task, consistently outperform models trained from scratch. Moreover, pretraining on pancancer datasets enables strong generalization across organs and tasks, outperforming slide foundation models while using substantially less pretraining data. These findings highlight the robust adaptability of MIL models and demonstrate the benefits of leveraging transfer learning to boost performance in CPath. Lastly, we provide a resource which standardizes the implementation of MIL models and collection of pretrained model weights on popular CPath tasks, available at this https URL
Abstract (translated)
多示例学习（MIL）是计算病理学（CPath）中的一个基石方法，用于从数十亿像素的组织图像中生成具有临床意义的幻灯片级别的嵌入。然而，在小规模、弱监督的临床数据集上，MIL常常面临挑战。与自然语言处理（NLP）和传统计算机视觉等领域不同，这些领域广泛使用迁移学习来解决数据稀缺问题，关于多示例学习模型的可迁移性知之甚少。 在本研究中，我们通过评估11个预训练模型在21项预训练任务中的表现来系统地评价了预训练MIL模型的迁移学习能力。这些任务涵盖形态和分子亚型预测。我们的结果显示，即使是在与目标任务不同的器官上进行训练，预训练过的MIL模型也始终优于从头开始训练的模型。此外，在泛癌症数据集上的预训练能够实现跨不同器官和任务的强大泛化性能，并且在使用显著较少的预训练数据的情况下超过了幻灯片基础模型。 这些发现强调了MIL模型的稳健适应性，展示了利用迁移学习提高CPath中性能的好处。最后，我们提供了一个资源，用于标准化流行CPath任务中的多示例学习模型实现和收集预训练模型权重，可在提供的网址上获取：[此链接]（请确保将实际URL替换为具体的可用链接）。
URL
https://arxiv.org/abs/2506.09022
