[论文解读] Semi-supervised few-shot learning for medical image segmentation

Abdur Razzaq Fayjie, Reza Azad+4 more|arXiv (Cornell University)|Mar 18, 2020

Domain Adaptation and Few-Shot Learning参考文献 27被引用 61

一句话总结

一个利用未标记图像通过代理任务来学习更具可迁移性的特征以用于医学图像分割的少样本语义分割框架，在皮肤病变数据集上进行了验证。

ABSTRACT

Recent years have witnessed the great progress of deep neural networks on semantic segmentation, particularly in medical imaging. Nevertheless, training high-performing models require large amounts of pixel-level ground truth masks, which can be prohibitive to obtain in the medical domain. Furthermore, training such models in a low-data regime highly increases the risk of overfitting. Recent attempts to alleviate the need for large annotated datasets have developed training strategies under the few-shot learning paradigm, which addresses this shortcoming by learning a novel class from only a few labeled examples. In this context, a segmentation model is trained on episodes, which represent different segmentation problems, each of them trained with a very small labeled dataset. In this work, we propose a novel few-shot learning framework for semantic segmentation, where unlabeled images are also made available at each episode. To handle this new learning paradigm, we propose to include surrogate tasks that can leverage very powerful supervisory signals --derived from the data itself-- for semantic feature learning. We show that including unlabeled surrogate tasks in the episodic training leads to more powerful feature representations, which ultimately results in better generability to unseen tasks. We demonstrate the efficiency of our method in the task of skin lesion segmentation in two publicly available datasets. Furthermore, our approach is general and model-agnostic, which can be combined with different deep architectures.

研究动机与目标

• 阐明在有限像素级注释下进行准确医学图像分割的必要性。
• 提出一个在每个迭代中也利用未标记图像的少样本分割框架。
• 引入从数据中衍生的代理任务以提供有力的监督信号。
• 证明所学特征对未见分割任务的泛化能力有所提升。

提出的方法

• 在表示不同分割问题的迭代中用少量标记示例训练分割模型。
• 在每个迭代中整合未标记图像以丰富监督信号。
• 引入利用数据衍生的监督信号进行特征学习的代理任务。
• 证明代理任务能够提升特征表示和泛化能力。
• 保持模型无关性兼容性，使该方法可以与不同架构结合。

实验结果

研究问题

• RQ1 在每个迭代中使用的未标记图像是否能提升少样本医学图像分割的表现？
• RQ2 基于数据的代理任务是否能改进对未见分割任务的学习特征表示？
• RQ3 所提框架是否与多种深度分割架构兼容？
• RQ4 该方法在公开的皮肤病变分割数据集上的表现如何？

主要发现

• 包含未标记的代理任务可获得更强的特征表示。
• 该方法提高了对未见分割任务的泛化能力。
• 该方法在两个公开可用的皮肤病变数据集上均有效。
• 该框架是模型无关的，可以与不同的网络架构结合。