WORKSHOP IS LIMITED WITH ONLY 20 PEOPLE. REGISTRATION IS CLOSED NOW!
Workshop Title: Carcinoma Classification from Medical Images with Deep Learning
Duration: 3 hours
09.00 – 09.50 Introduction to Carcinoma Classification
Fundamentals of Deep Learning and Medical Images
09.50 – 10.00 Coffee Break
10.00 – 12.00 Preprocessing and Data Augmentation
Building the Carcinoma Classification Model
Model Evaluation and Interpretation
Recap
Target Audience:
– Participants with basic knowledge of deep learning and image processing
– Medical professionals interested in applying AI to diagnostics
– Data scientists and machine learning enthusiasts
Workshop Outline:
1. Introduction to Carcinoma Classification (20 minutes)
– Brief overview of carcinoma and its diagnostic challenges
– Role of AI in medical image analysis
2. Fundamentals of Deep Learning and Medical Images (30 minutes)
– Introduction to deep learning concepts
– Challenges and characteristics of medical image data (noise, resolution, variability)
– Dataset overview
– Preprocessing and Data Augmentation (resizing, normalisation, augmentation)
3. Building the Carcinoma Classification Model (1 hour)
– Introduction to convolutional neural networks (CNNs) and their role in image classification
– Designing the architecture of the carcinoma classification model (discuss various architectures like VGG, ResNet, etc.)
– Hands-on: Building and training the CNN model on the provided dataset
4. Model Evaluation and Interpretation (20 minutes)
– Explaining common evaluation metrics for classification tasks (accuracy, precision, recall, F1-score)
– Visualization techniques for model interpretation
– Hands-on: Evaluating the trained model’s performance and interpreting results
5. Overcoming Challenges and Improving Performance (20 minutes)
– Discussing common challenges in medical image analysis (class imbalance, limited data)
– Techniques for addressing challenges (data augmentation, transfer learning, fine-tuning)
– Brief discussion on ethical considerations in medical AI
6. Future Directions and Conclusion (20 minutes)
– Resources for further learning
– Summarising key takeaways from the workshop and Q&A
Note: This workshop only covers the key concepts and provides a hands-on experience with building and evaluating a carcinoma classification model using deep learning.