Index
Image-to-Image Translation Using Latent Diffusion Models
themaAutomated Configuration of U-Net Architecture for Medical Image Segmentation
In this research, we will explore how different U-Net hyperparameters impact on segmentation performance in multiple medical segmentation datasets. By combining the frameworks of MONAI and nnU-Net, the study will investigate how to effectively adjust relevant hyperparameters of U-Net to optimize the model ‘s performance in different medical image segmentation tasks. Specifically, we will focus on analyzing the impact of hyperparameters such as network depth, convolution kernel size, learning rate and data augmentation strategies on segmentation performance based on U-Net architecture, and validate the effectiveness of these hyperparameters settings per experiment. Ultimately through systematic research and experiments, we aim to provide a more efficient and highly generalizable U-Net model configuration scheme for medical image segmentation tasks.
The purpose of this study is to explore and optimize the hyperparameter configuration of the U-Net model architecture to improve the performance in various medical image segmentation tasks, such as binary and multi-class medical image dataset segmentation. Through systematic experiment and analysis, we will seek to gain a deep undetstanding of how different hyperparameter settings impact on the result of image segmentation, thereby providing more efficient and generalizable solutions for medical image segmentation tasks. The potential outcomes of this research will not only improve accuracy and precision of image segmentation but also provide valuable references and support for researchers in relevant fields.