Index

Automated 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.

Assessing the Impact of LLMs on Reduction of Supplier-Related Warranty Costs of Siemens Healthineers’ Global Supply Chain

Medical Image Foundation Model for MR Abnormalities

Advanced LLM Prompting for Patient-Tailored CT Protocol Adjustment

Enhance MRI Reliability by leveraging GANs to learn corrupted Images

CLICK-SPOT: Detection and Classification of Cetacean Echolocation Clicks using Image-based Object Detection Methods applied to Advanced Wavelet-based Transformations

Detection of Alzheimer’s disease and depression in speech by Graph Neural Networks

Ensuring Quality of Bots Powered by Generative Artificial Intelligence with Automated AI-Persona-Based Testing

xLSTM: Extended Long Short-Term Memory for Enhanced Performance and Scalability

Automatic Data Augmentation for Multi organ Segmentation

Various medical image segmentation models have been created in the past few years for both supervised and semi-supervised tasks. Since variability and diversity of data play a huge role in the accuracy of such models, choosing the most appropriate data augmentation strategy for each organ or tumor is very important in improving the model outcomes. However, most of these data augmentation strategies used in UNet and its variants are simple, custom-made, and not fully optimal. This manual selection of data augmentation strategies limits the possibility for improvement of the accuracy of the medical image segmentation.
As a further improvement on this, Yang et al [1] proposed a technique for the automation data augmentation strategy selection using reinforcement learning. However, this technique requires a high computational time and only tells about the probability of each augmentation strategy. To overcome this issue, Xu et al. [2] proposed an automatic data augmentation strategy framework called ASNG, which searches for the most optimal augmentation strategy by formulating a bi-level optimization algorithm. This framework also designed a search space that includes the fixed magnitude of the operation and the interval of magnitude. Moreover, this work showcases the dynamic change of strategies during training as per requirement, achieving a state-of-the-art performance.
The thesis aims to research the following:
1. How different data augmentation strategies influence the segmentation performance in different data conditions. The data conditions are based on object shape/size, number of objects, mean and standard deviation of the intensity, and so on.
2. Perform analysis of the correlation of the data conditions and the augmentation methods. For this, first, a search space is to be defined which includes typical augmentation strategies with varying hyperparameters, similar to the work done by He et al [3] and Cubuk et al [4]. The performance of different augmentation methods is tested on a segmentation model built using the MONAI framework.
3. Implement an algorithm for searching strategy using machine learning regressor models like support vector regressor that will lead to automatic planning of the augmentation pipeline based on the target dataset. The dataset used for the algorithm development is AMOS [5]. AMOS is a large-scale, diverse, clinical dataset comprising CT and MRI scans for 15 abdominal organ segmentation.