An Exploration of Data Augmentation Techniques in Ensemble Learning for Medical Image Segmentation with Transfer Learning

this paper examines using data augmentation strategies in the ensemble, getting to know medical photo segmentation with transfer learning. Various transfer-gaining knowledge of techniques, namely pretrained models, unsupervised function mastering, and multitasking studying, are explored. Pre-skilled models are skilled in one area and further high-quality-tuned using information from any other area to enhance segmentation overall performance. Unsupervised characteristic learning creates a common characteristic space that encodes the shared styles between numerous datasets. Multitask mastering combines challenge-particular multitasking getting to know, and feature-particular studying into a single, more accurate version. Records augmentation strategies unique to scientific photos, such as random cropping, random flipping, random rotation, and affine transformation, are mentioned. The effectiveness of different records augmentation strategies is evaluated on several scientific datasets, such as liver and lung datasets. Effects show combining statistics augmentation techniques with ensemble learning can drastically enhance segmentation accuracy. The look presents further evidence that information augmentation strategies can correctly be used for the clinical image segmentation venture.