基于组合解码器的通用医学图像表示学习
Universal Medical Image Representation Learning with Compositional Decoders
AI总结：
针对医学成像通用模型受功能需求、数据限制且灵活性不足的问题，提出带分解与组合解码器的UniMed范式，结合联合表示学习实现高效预训练，在多任务多数据集上达SOTA且迁移性强。
摘要：
视觉-语言模型推动了通用模型的发展，但其在医学成像领域的应用仍受限于特定功能需求与数据稀缺的问题。当前的通用模型通常配备任务专属的分支与头部，这限制了共享特征空间的范围与模型的灵活性。为应对这些挑战，我们提出了一种分解-组合式通用医学成像范式（UniMed），可支持所有层级的任务。为此，我们首先设计了分解解码器，它能基于定义的输入队列预测两类输出——像素级与语义级输出。此外，我们引入了组合解码器，用于统一输入与输出空间，并将不同层级的任务标注标准化为离散token格式。这两个组件的耦合设计使模型能够灵活组合任务并实现互利。另外，我们的联合表示学习策略巧妙利用了大量无标注数据与无监督损失，实现了高效的单阶段预训练，以获得更鲁棒的性能。实验结果表明，UniMed在三类任务共8个数据集上均取得了当前最优性能，且展现出强大的零样本与100样本迁移能力。我们将在论文录用后发布代码与训练好的模型。
英文摘要：
Visual-language models have advanced the development of universal models, yet their application in medical imaging remains constrained by specific functional requirements and the limited data. Current general-purpose models are typically designed with task-specific branches and heads, which restricts the shared feature space and the flexibility of model. To address these challenges, we have developed a decomposed-composed universal medical imaging paradigm (UniMed) that supports tasks at all levels. To this end, we first propose a decomposed decoder that can predict two types of outputs -- pixel and semantic, based on a defined input queue. Additionally, we introduce a composed decoder that unifies the input and output spaces and standardizes task annotations across different levels into a discrete token format. The coupled design of these two components enables the model to flexibly combine tasks and mutual benefits. Moreover, our joint representation learning strategy skilfully leverages large amounts of unlabeled data and unsupervised loss, achieving efficient one-stage pretraining for more robust performance. Experimental results show that UniMed achieves state-of-the-art performance on eight datasets across all three tasks and exhibits strong zero-shot and 100-shot transferability. We will release the code and trained models upon the paper's acceptance.
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。
