Exploring the Performance of Meta Learning Strategies for Medical Image Segmentation with Transfer Learning

This paper explores the overall performance of meta-gaining knowledge of strategies for medical photo segmentation with switch mastering. It discusses the latest advancements in deep gaining knowledge of and switching learning strategies and their software to clinical photo segmentation. Specifically, the examiner evaluates meta-studying strategies, especially for clinical image segmentation, which aim to optimize the parameters of conventional transfer learning strategies. First, the paper opinions the unique varieties of switch learning strategies, which include pre-skilled models, area version, transfer studying inside the convolution neural networks (CNNs), and multi-undertaking getting to know, and affords an outline in their packages. Then, numerous meta-studying strategies are mentioned, including reinforcement mastering, multi-assignment getting to know, version ensembles, and meta-gaining knowledge of algorithms. They have a look at evaluating those meta-getting-to-know strategies on three public clinical photo segmentation datasets: GlaS, okay, and Acropolis. It concludes that the proposed meta-learning strategies yield massive performance improvements compared to traditional transfer learning strategies, particularly for datasets with significant area shifts.