论文标题
统一的表示学习进行有效的医学图像分析
Unified Representation Learning for Efficient Medical Image Analysis
论文作者
论文摘要
医疗图像分析通常包括几个任务，例如增强，分割和分类。传统上，这些任务是使用单独的深度学习模型来实现的，这是不高效的，因为它涉及不必要的培训重复，需要更多的计算资源，并且需要相对较大的标记数据。在本文中，我们提出了一种用于医学图像分析的多任务培训方法，其中通过相关的知识转移使用统一模式特定的特征特征表示（UMS-REP）来同时对单个任务进行微调。我们探索不同的微调策略，以证明该策略对目标医学图像任务的性能的影响。我们尝试不同的视觉任务（例如，图像denoising，分割和分类），以突出我们对两种成像方式（胸部X射线和多普勒超声心动图）提供的优势。我们的结果表明，所提出的方法减少了对计算资源的总体需求，并改善了目标任务概括和绩效。此外，我们的结果证明，医学图像中目标任务的执行受到利用的微调策略的极大影响。
Medical image analysis typically includes several tasks such as enhancement, segmentation, and classification. Traditionally, these tasks are implemented using separate deep learning models for separate tasks, which is not efficient because it involves unnecessary training repetitions, demands greater computational resources, and requires a relatively large amount of labeled data. In this paper, we propose a multi-task training approach for medical image analysis, where individual tasks are fine-tuned simultaneously through relevant knowledge transfer using a unified modality-specific feature representation (UMS-Rep). We explore different fine-tuning strategies to demonstrate the impact of the strategy on the performance of target medical image tasks. We experiment with different visual tasks (e.g., image denoising, segmentation, and classification) to highlight the advantages offered with our approach for two imaging modalities, chest X-ray and Doppler echocardiography. Our results demonstrate that the proposed approach reduces the overall demand for computational resources and improves target task generalization and performance. Further, our results prove that the performance of target tasks in medical images is highly influenced by the utilized fine-tuning strategy.
