Meta-Learners for Few-Shot Weakly-Supervised Medical Image Segmentation
This work addresses the problem of segmenting medical images with limited annotations for radiologists, but it is incremental as it adapts existing meta-learning paradigms to a new task.
The paper tackled few-shot weakly-supervised medical image segmentation by proposing a generic meta-learning framework and comparing nine meta-learners across various radiological tasks, finding that metric-based approaches perform better with smaller domain shifts while gradient- and fusion-based methods generalize better to larger shifts.
Most uses of Meta-Learning in visual recognition are very often applied to image classification, with a relative lack of works in other tasks {such} as segmentation and detection. We propose a generic Meta-Learning framework for few-shot weakly-supervised segmentation in medical imaging domains. We conduct a comparative analysis of meta-learners from distinct paradigms adapted to few-shot image segmentation in different sparsely annotated radiological tasks. The imaging modalities include 2D chest, mammographic and dental X-rays, as well as 2D slices of volumetric tomography and resonance images. Our experiments consider a total of 9 meta-learners, 4 backbones and multiple target organ segmentation tasks. We explore small-data scenarios in radiology with varying weak annotation styles and densities. Our analysis shows that metric-based meta-learning approaches achieve better segmentation results in tasks with smaller domain shifts in comparison to the meta-training datasets, while some gradient- and fusion-based meta-learners are more generalizable to larger domain shifts.