Advanced Computer Vision

This course is designed to provide an in-depth understanding of advanced computer vision techniques and their applications in the Healthcare & Life Sciences industries. Participants will learn cutting-edge methods for object detection, semantic segmentation, and 3D reconstruction, enabling them to develop and deploy sophisticated computer vision solutions for various tasks relevant to the healthcare and life sciences domains, such as medical image analysis, digital pathology, and drug discovery.

• Understand the principles and techniques behind advanced computer vision methods

• Implement and train state-of-the-art object detection models, such as Faster R-CNN, YOLO, and SSD, for medical object detection

• Apply semantic segmentation algorithms, such as U-Net, DeepLab, and Mask R-CNN, for pixel-wise classification in medical images

• Reconstruct 3D models from 2D medical images using structure from motion (SfM) and multi-view stereo (MVS) techniques

• Develop and deploy advanced computer vision solutions for healthcare and life sciences applications, such as disease diagnosis and drug discovery

1. Object Detection in Medical Images

• Overview of object detection and its applications in the Healthcare & Life Sciences industries

• Two-stage object detectors: R-CNN, Fast R-CNN, and Faster R-CNN

• Single-stage object detectors: YOLO, SSD, and RetinaNet

• Hands-on exercises: Implementing and training object detection models on medical image datasets

2. Semantic Segmentation for Medical Image Analysis

• Introduction to semantic segmentation and its importance in healthcare and life sciences

• Fully Convolutional Networks (FCNs) for semantic segmentation

• Encoder-decoder architectures: U-Net, SegNet, and DeepLab

• Instance segmentation with Mask R-CNN for cell and nuclei detection

• Hands-on exercises: Applying semantic segmentation models to medical and biological images

3. 3D Reconstruction and Visualization

• Principles of 3D reconstruction from 2D medical images

• Structure from Motion (SfM) and feature matching techniques

• Multi-View Stereo (MVS) and dense reconstruction methods

• Point cloud processing and mesh generation for medical visualization

• Hands-on exercises: Reconstructing 3D models of anatomical structures and biological samples

4. Advanced Topics and Applications

• Attention mechanisms and transformer-based models for medical image analysis

• Domain adaptation and transfer learning for cross-modality image analysis

• Unsupervised and self-supervised learning for representation learning in healthcare data

• Case studies of advanced computer vision in healthcare and life sciences (e.g., digital pathology, drug discovery)

• Hands-on exercises: Developing an advanced computer vision solution for a specific healthcare or life sciences use case

5. Deployment and Optimization

• Deploying computer vision models in production environments

• Optimizing models for real-time inference and edge deployment

• Monitoring and updating deployed models for continuous improvement

• Best practices for data management and version control in computer vision projects

• Hands-on exercises: Deploying an optimized computer vision model using a cloud platform (e.g., AWS, GCP)

• Strong understanding of linear algebra, calculus, and probability theory

• Proficiency in programming with Python and deep learning frameworks (e.g., TensorFlow, PyTorch)

• Familiarity with basic computer vision concepts and techniques (e.g., image processing, feature extraction)

• Knowledge of convolutional neural networks (CNNs) and their applications