Computer Vision for Medical Imaging

• Introduction to computer vision
• Why computer vision is important in healthcare
• Overview of medical image analysis
• Types of medical images
• Difference between natural images and medical images
• Basic image processing concepts
• Clinical use cases of AI in medical imaging

নেবেন: Md. Tanvir Hayat

• Digital image representation
• Pixels. voxels. slices. volumes
• Image channels and grayscale intensity
• Resolution. contrast. brightness
• Texture. edges. shape. and patterns in medical images
• Anatomical structures in images
• Noise. artifacts. and distortions
• Image histogram and intensity distribution
• Region-based and structural interpretation
• DICOM. NIfTI. and medical image formats

নেবেন: Md. Tanvir Hayat

• Image normalization
• Resizing and resampling
• Denoising
• Contrast enhancement
• Filtering techniques
• Data augmentation
• ROI extraction
• Patch generation
• Handling class imbalance
• Preparing data for training

নেবেন: Md. Tanvir Hayat

• Machine Learning vs Deep Learning in Medical Imaging Models: Support Vector Machine (SVM). Random Forest (RF). K-Nearest Neighbors (KNN). Logistic Regression. Multilayer Perceptron (MLP)
• CNN Fundamentals Models/Architectures: LeNet. AlexNet. VGGNet. GoogLeNet. ResNet
• Feature Extraction Techniques/Models: Handcrafted Features. HOG. SIFT. LBP. GLCM. Deep CNN Feature Embeddings
• Transfer Learning Pretrained Models: VGG16. VGG19. ResNet50. DenseNet121. InceptionV3. EfficientNet. MobileNet
• Training Pipelines for Medical Data Framework-oriented modeling practice: PyTorch pipeline. TensorFlow/Keras pipeline. MONAI-based medical imaging workflow

নেবেন: Md. Tanvir Hayat

• 2D CNN and 3D CNN Models: 2D CNN. 3D CNN. 3D ResNet. 3D DenseNet. V-Net
• U-Net and Segmentation Models Models: U-Net. U-Net++. Attention U-Net. 3D U-Net. V-Net. nnU-Net
• Detection Models Models: Faster R-CNN. Mask R-CNN. YOLO. RetinaNet. SSD
• Attention Mechanisms Models/Methods: SE-Net. CBAM. Attention U-Net. Vision Transformer (ViT). Swin Transformer
• Multimodal Learning Models/Approaches: CNN + Clinical Data Fusion. CNN-LSTM. Multimodal Transformer. Hybrid Fusion Networks

নেবেন: Md. Tanvir Hayat

• Introduction to explainable AI using CAM and Grad-CAM
• Visual interpretation of model predictions using Grad-CAM. Guided Grad-CAM. and Saliency Maps
• Feature importance analysis using SHAP and LIME
• Attention-based explanation using Attention Map Visualization
• Model behavior analysis using Occlusion Sensitivity
• Clinical trust. reliability. and interpretability in medical imaging models

নেবেন: Md. Tanvir Hayat