Conventional deep learning models deal with images one-by-one, requiring
costly and time-consuming expert labeling in the field of medical imaging, and
domain-specific restriction limits model generalizability. Visual in-context
learning (ICL) is a new and exciting area of research in computer vision.
Unlike conventional deep learning, ICL emphasizes the model's ability to adapt
to new tasks based on given examples quickly. Inspired by MAE-VQGAN, we
proposed a new simple visual ICL method called SimICL, combining visual ICL
pairing images with masked image modeling (MIM) designed for self-supervised
learning. We validated our method on bony structures segmentation in a wrist
ultrasound (US) dataset with limited annotations, where the clinical objective
was to segment bony structures to help with further fracture detection. We used
a test set containing 3822 images from 18 patients for bony region
segmentation. SimICL achieved an remarkably high Dice coeffient (DC) of 0.96
and Jaccard Index (IoU) of 0.92, surpassing state-of-the-art segmentation and
visual ICL models (a maximum DC 0.86 and IoU 0.76), with SimICL DC and IoU
increasing up to 0.10 and 0.16. This remarkably high agreement with limited
manual annotations indicates SimICL could be used for training AI models even
on small US datasets. This could dramatically decrease the human expert time
required for image labeling compared to conventional approaches, and enhance
the real-world use of AI assistance in US image analysis.

新的简单视觉上下文学习（ICL）方法 SimICL 结合被设计用于自监督学习的屏蔽图像建模（MIM）对配对图像进行视觉 ICL，成功地在具有有限注释的腕部超声数据集上验证，对于骨骼结构分割取得了非常高的 Dice 系数（DC）和 Jaccard 指数（IoU），并能减少图像标注需要的人工专家时间，提高超声图像分析中人工智能辅助的实际应用。