Semi-supervised Contrastive Learning for Label-efficient Medical Image Segmentation
This work addresses the challenge of reducing annotation effort for medical image segmentation, which is critical for healthcare applications, but it is incremental as it builds on existing contrastive learning approaches.
The paper tackles the problem of label-efficient medical image segmentation by proposing a semi-supervised contrastive learning method that incorporates limited pixel-wise annotations during pre-training, resulting in consistent performance improvements over state-of-the-art methods across two biomedical datasets with varying labeled data amounts.
The success of deep learning methods in medical image segmentation tasks heavily depends on a large amount of labeled data to supervise the training. On the other hand, the annotation of biomedical images requires domain knowledge and can be laborious. Recently, contrastive learning has demonstrated great potential in learning latent representation of images even without any label. Existing works have explored its application to biomedical image segmentation where only a small portion of data is labeled, through a pre-training phase based on self-supervised contrastive learning without using any labels followed by a supervised fine-tuning phase on the labeled portion of data only. In this paper, we establish that by including the limited label in formation in the pre-training phase, it is possible to boost the performance of contrastive learning. We propose a supervised local contrastive loss that leverages limited pixel-wise annotation to force pixels with the same label to gather around in the embedding space. Such loss needs pixel-wise computation which can be expensive for large images, and we further propose two strategies, downsampling and block division, to address the issue. We evaluate our methods on two public biomedical image datasets of different modalities. With different amounts of labeled data, our methods consistently outperform the state-of-the-art contrast-based methods and other semi-supervised learning techniques.