Use AI to create brand stories, analyze brand reputation
課程概述與目標：
本課程教導學生使用網路爬蟲與文本分析技巧分析品牌聲譽，並使用AI去模擬創建一個品牌，並將它加以營運。
Use AI to create brand stories, analyze brand reputation
課程概述與目標：
本課程教導學生使用網路爬蟲與文本分析技巧分析品牌聲譽，並使用AI去模擬創建一個品牌，並將它加以營運。
| Topic | Content outline | Teach | Demonstration | Exercises | Others |
|---|---|---|---|---|---|
| Principles and Diagnosis Overview | Overview of medical imaging modalities and principles, introduction to example diseases (autism, pneumonia, dementia, diabetes mellitus…) in class | 1hr | 0hr | 0hr | 0hr |
| Data preprocessing | Focus on how to preprocess with Python to make into Pytorch dataloader Explain two examples: MRI and Histo Data preprocessing lab: give each team a dataset and prepare a pytorch dataloader (will use later for group project) | 0.5hr | 0.5hr | 1hr | - |
| Computer Vision with Deep Learning Introduction | This session covers the fundamentals of computer vision and deep learning, including structures of models, feature extraction, image classification, and hands-on implementation of medical image analysis models. | 0.5hr | 0.5hr | - | - |
| Image Classification Colab Tutorial | This session provides a hands-on tutorial using Google Colab to implement image classification models, covering data preprocessing, model training, evaluation, and visualization of results. | - | 0.5hr | 0.5hr | - |
| Results Interpretation and Reasoning | This session focuses on interpreting AI model results by integrating medical knowledge of diseases and clinical contexts, analyzing performance metrics, visualizing decision-making processes, and making informed inferences to support clinical decision-making. | 0.5hr | 0.5hr | - | - |
| Group project presentations | This session features group project presentations where participants apply and integrate the skills learned throughout the course, showcasing their end-to-end AI model development, from data preprocessing and training to result interpretation and clinical reasoning. | - | - | 2hr | - |
| 日期 | 課程進度、內容、主題 |
|---|---|
| 03/22 | 1.1 Introduction to medical imaging modalities (MRI, CT, X-ray, Retinal Imaging, Histopathology) 1.2 Fundamental principles behind different imaging techniques 1.3 Common medical conditions analyzed through imaging (e.g., tumors, cardiovascular diseases, retinal disorders; autism, pneumonia, dementia) 1.4 Role of AI in medical image diagnosis and clinical decision-making 2.1 Importance of data preprocessing in AI-driven medical imaging 2.2 Image normalization, augmentation, and enhancement techniques 2.3 Handling missing data and noise reduction methods 2.4 Dataset loading, preprocessing, and augmentation in Python |
| 03/23 | 3.1 Fundamentals of computer vision and its applications in healthcare 3.2 Overview of deep learning architectures for medical imaging (CNNs, transformers, hybrid models) 3.3 Feature extraction and representation learning in medical image analysis 3.4 Challenges and limitations of deep learning in clinical practice 4.1 Hands-on implementation of image classification models using Google Colab 4.2 Training deep learning models for disease classification (e.g., normal vs. abnormal scans) 4.3 Performance evaluation with key metrics (accuracy, precision, recall, AUC-ROC) 5.1 Understanding AI model predictions and performance metrics 5.2 Visualization techniques (Grad-CAM) for explainable AI in medical imaging 5.3 Integrating clinical knowledge with AI results for diagnosis and decision-making 5.4 Ethical considerations and bias detection in medical AI applications |
| 03/29 | 6.1 Presentation of team-based AI projects on medical image analysis 6.2 Demonstration of end-to-end model development, from preprocessing to interpretation 6.3 Peer review and feedback session to improve clinical applicability of AI models 6.4 Discussion on challenges, lessons learned, and future improvements |