基于深度学习的生物医学图像分割：使用UNet架构与迁移学习
Deep Learning-based Bio-Medical Image Segmentation using UNet Architecture and Transfer Learning
AI总结：
本文基于Pytorch从零实现UNet架构，并引入迁移学习对改进的UNet分割包进行微调，在多个生物医学图像数据集上的实验表明迁移学习模型性能更优。
摘要：
图像分割是计算机视觉的一个分支，广泛应用于包括生物医学图像处理在内的现实世界应用中。随着深度学习的最新进展，图像分割已达到非常高的性能水平。最近，UNet架构被发现是新型深度学习分割方法的核心。在本文中，我们从零开始实现UNet架构，使用Pytorch中的基本模块，并在多个生物医学图像数据集上评估其性能。我们还使用迁移学习，将新型改进的UNet分割包应用于生物医学图像数据集。我们针对每个特定数据集对预训练的迁移模型进行微调。我们将其性能与我们从零实现的基础UNet进行比较。结果表明，迁移学习模型在图像分割中的性能优于从零实现的UNet模型。
英文摘要：
Image segmentation is a branch of computer vision that is widely used in real world applications including biomedical image processing. With recent advancement of deep learning, image segmentation has achieved at a very high level performance. Recently, UNet architecture is found as the core of novel deep learning segmentation methods. In this paper we implement UNet architecture from scratch with using basic blocks in Pytorch and evaluate its performance on multiple biomedical image datasets. We also use transfer learning to apply novel modified UNet segmentation packages on the biomedical image datasets. We fine tune the pre-trained transferred model with each specific dataset. We compare its performance with our fundamental UNet implementation. We show that transferred learning model has better performance in image segmentation than UNet model that is implemented from scratch.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
