论文标题
理想：半监督医学图像细分的密集密集的局部对比度学习
IDEAL: Improved DEnse locAL Contrastive Learning for Semi-Supervised Medical Image Segmentation
论文作者
论文摘要
由于标有数据的稀缺性，对比反之为的自我监督学习（SSL）框架最近在几项医学图像分析任务中显示出巨大的潜力。但是，现有的对比机制对于密集的像素级分割任务是不最佳的，因为它们无法挖掘局部特征。为此，我们使用密集的（DIS）相似性学习将度量学习的概念扩展到细分任务，以预先培训深度编码器网络，并采用半监督的范式来微调下游任务。具体而言，我们提出了一个简单的卷积投影头，用于获得密集的像素级特征，并提出了一种新的对比损失来利用这些密集的投影，从而改善了局部表示。为下游任务设计了涉及两流模型训练的双向一致性正规化机制。在比较时，我们的理想方法在心脏MRI分割方面优于SOTA方法。可用代码：https：//github.com/hritam-98/ideal-icassp23
Due to the scarcity of labeled data, Contrastive Self-Supervised Learning (SSL) frameworks have lately shown great potential in several medical image analysis tasks. However, the existing contrastive mechanisms are sub-optimal for dense pixel-level segmentation tasks due to their inability to mine local features. To this end, we extend the concept of metric learning to the segmentation task, using a dense (dis)similarity learning for pre-training a deep encoder network, and employing a semi-supervised paradigm to fine-tune for the downstream task. Specifically, we propose a simple convolutional projection head for obtaining dense pixel-level features, and a new contrastive loss to utilize these dense projections thereby improving the local representations. A bidirectional consistency regularization mechanism involving two-stream model training is devised for the downstream task. Upon comparison, our IDEAL method outperforms the SoTA methods by fair margins on cardiac MRI segmentation. Code available: https://github.com/hritam-98/IDEAL-ICASSP23
