面向体积器官分割的基础模型与少样本参数高效微调
Towards Foundation Models and Few-Shot Parameter-Efficient Fine-Tuning for Volumetric Organ Segmentation
AI总结：
本文针对医学图像分割中标注数据和计算资源受限的问题，提出少样本高效微调（FSEFT）场景，并引入空间黑盒适配器与约束转导推理等方法，在预训练基础模型上实现数据和参数双高效适配。
摘要：
近年来，基础模型的流行以及“预训练-适配”范式（即将大规模模型迁移到下游任务）在体积医学图像分割领域日益受到关注。然而，当前针对迁移学习的微调策略通常采用全量微调，这可能需要大量资源，并且在目标任务的标注数据稀缺时往往产生次优结果。这使得其在真实临床环境中的适用性面临挑战，因为这些机构通常在数据和计算资源方面受到限制，难以开发专有解决方案。为应对这一挑战，我们形式化了少样本高效微调（Few-Shot Efficient Fine-Tuning, FSEFT），这是一种用于适配医学图像分割基础模型的新颖且贴近现实的场景。该设定同时考虑了适配过程中数据效率和参数效率的关键作用。基于一个在开放获取的CT器官分割数据源上预训练的基础模型，我们提出利用参数高效微调（Parameter-Efficient Fine-Tuning）和黑盒适配器（black-box Adapters）来应对上述挑战。此外，本文还引入了新颖的高效适配方法，包括更适合密集预测任务的空间黑盒适配器（Spatial black-box Adapters），以及利用任务特定先验知识的约束转导推理。我们全面的迁移学习实验证实了基础模型在医学图像分割中的适用性，并揭示了当前流行的微调策略在少样本场景下的局限性。
英文摘要：
The recent popularity of foundation models and the pre-train-and-adapt paradigm, where a large-scale model is transferred to downstream tasks, is gaining attention for volumetric medical image segmentation. However, current transfer learning strategies devoted to full fine-tuning for transfer learning may require significant resources and yield sub-optimal results when the labeled data of the target task is scarce. This makes its applicability in real clinical settings challenging since these institutions are usually constrained on data and computational resources to develop proprietary solutions. To address this challenge, we formalize Few-Shot Efficient Fine-Tuning (FSEFT), a novel and realistic scenario for adapting medical image segmentation foundation models. This setting considers the key role of both data- and parameter-efficiency during adaptation. Building on a foundation model pre-trained on open-access CT organ segmentation sources, we propose leveraging Parameter-Efficient Fine-Tuning and black-box Adapters to address such challenges. Furthermore, novel efficient adaptation methodologies are introduced in this work, which include Spatial black-box Adapters that are more appropriate for dense prediction tasks and constrained transductive inference, leveraging task-specific prior knowledge. Our comprehensive transfer learning experiments confirm the suitability of foundation models in medical image segmentation and unveil the limitations of popular fine-tuning strategies in few-shot scenarios.
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