A5083126306- Advanced Image Processing Techniques
- Brain Tumor Detection and Classification
- Generative Adversarial Networks and Image Synthesis
- Advanced Computing and Algorithms
- Energy Load and Power Forecasting
- Autism Spectrum Disorder Research
- Stock Market Forecasting Methods
- Domain Adaptation and Few-Shot Learning
- Advanced Neuroimaging Techniques and Applications
- Medical Image Segmentation Techniques
- Cell Image Analysis Techniques
- Advanced Neural Network Applications
- AI in cancer detection
- Radiomics and Machine Learning in Medical Imaging
- Forecasting Techniques and Applications
- Assistive Technology in Communication and Mobility
- Image and Signal Denoising Methods
- Child Development and Digital Technology
Shandong Institute of Business and Technology
2022-2023
Statistics show that each year more than 100,000 patients pass away from brain tumors. Due to the diverse morphology, hazy boundaries, or unbalanced categories of medical data lesions, segmentation prediction tumors has significant challenges.In this thesis, we highlight EAV-UNet, a system designed accurately detect lesion regions. Optimizing feature extraction, utilizing automatic techniques anomalous regions, and strengthening structure. We prioritize problem especially in cases where...
Today's brain imaging modality migration techniques are transformed from one data in domain to another. In the specific clinical diagnosis, multiple modal can be obtained same scanning field, and it is more beneficial synthesize missing by using diversity characteristics of data. Therefore, we introduce a self-supervised learning cycle-consistent generative adversarial network (BSL-GAN) for transfer. The framework constructs multi-branch input, which enables learn multimodal addition, their...
Recently, attention has been drawn toward brain imaging technology in the medical field, among which MRI plays a vital role clinical diagnosis and lesion analysis of diseases. Different sequences MR images provide more comprehensive information help doctors to make accurate diagnoses. However, their costs are particularly high. For many image-to-image synthesis methods supervised learning-based require labeled datasets, often difficult obtain. Therefore, we propose an unsupervised generative...
Autism Spectrum Disorder (ASD) significantly impacts the quality of life for individuals and imposes burdens on their families society. Therefore, accurately identifying autism in early stages providing effective treatment patients becomes particularly crucial. This paper proposes a prediction model based multimodal features multiple classifiers. In this study, two imaging modalities, resting-state functional magnetic resonance imaging(fMRI) structural (MRI), were employed as features. After...
MRI plays a crucial role in clinical diagnosis and lesion analysis. MR images from different sequences provide richer information, aiding healthcare professionals making accurate diagnoses. This paper proposes self-supervised Generative Adversarial Network (SC-GAN) framework, which synthesizes Apparent Diffusion Coefficient (ADC) T2 Turbo Inversion Recovery Magnitude (T2 TIRM) images. The SC-GAN model introduced this does not require paired dataset, instead utilizing self-features as...