Which images to label for few-shot medical landmark detection?
This addresses the challenge of reducing annotation burden for radiologists in medical imaging by optimizing template selection in few-shot learning, though it is incremental as it builds on existing few-shot methods.
The paper tackles the problem of selecting which images to label for few-shot medical landmark detection to improve performance, proposing a Sample Choosing Policy (SCP) that reduces mean radial errors by 14.2% on the Cephalometric dataset and 35.5% on the HandXray dataset in one-shot scenarios.
The success of deep learning methods relies on the availability of well-labeled large-scale datasets. However, for medical images, annotating such abundant training data often requires experienced radiologists and consumes their limited time. Few-shot learning is developed to alleviate this burden, which achieves competitive performances with only several labeled data. However, a crucial yet previously overlooked problem in few-shot learning is about the selection of template images for annotation before learning, which affects the final performance. We herein propose a novel Sample Choosing Policy (SCP) to select "the most worthy" images for annotation, in the context of few-shot medical landmark detection. SCP consists of three parts: 1) Self-supervised training for building a pre-trained deep model to extract features from radiological images, 2) Key Point Proposal for localizing informative patches, and 3) Representative Score Estimation for searching the most representative samples or templates. The advantage of SCP is demonstrated by various experiments on three widely-used public datasets. For one-shot medical landmark detection, its use reduces the mean radial errors on Cephalometric and HandXray datasets by 14.2% (from 3.595mm to 3.083mm) and 35.5% (4.114mm to 2.653mm), respectively.