Meta-Learners for Few-Shot Weakly-Supervised Medical Image Segmentation

• URL: http://arxiv.org/abs/2305.06912v1
• Date: Thu, 11 May 2023 15:57:45 GMT
• Title: Meta-Learners for Few-Shot Weakly-Supervised Medical Image Segmentation
• Authors: Hugo Oliveira, Pedro H. T. Gama, Isabelle Bloch, Roberto Marcondes Cesar Jr
• Abstract summary: 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 adapted to few-shot image segmentation in different sparsely annotated radiological tasks.
• Score: 2.781492199939609
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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