Introduction to Computational Pathology: Concepts and Importance Overview of computational pathology, its key concepts, and its relevance to modern pathology practices.
09.40-10.10
Understanding Digital Images: The Building Blocks of Computational Pathology Explanation of what an image is, the concept of pixels, resolution, colour spaces, stain vector estimation, and how images are represented and processed. Introduces compression, whole slide imaging (WSI) and the concept of pyramidal images.
10.10-10.40
Image Processing in Pathology: Techniques and Whole Slide Imaging Covers foundational image processing techniques such as thresholding, filtering, and morphological operations. Introduce segmentation, object identification and feature extraction.
10.40-11.00
Coffee Break (20 mins)
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Ghost Tour of Oxford
11.00 - 11.30
Feature Extraction in Computational Pathology: Feature engineering Introduction to traditional heuristic feature extraction techniques, such as texture, shape, intensity, and morphology. Discuss their applications in building classical computational pathology applications.
11.30 - 12.00
Introduction to Deep Learning Introduce concepts like neural networks, forward and back prop, loss function, gradient descent, training, and inference. Introduce methodologies training, validation and test data sets. Introduce performance measurements: clinical relevance v. stats.
12.00 - 12.30
Neural Nets in Computational Pathology Introduction of convolution. Bestiary of architectures: ConvNets, Transformers, ViTs
12.30-13.30
Lunch Break (1 Hour)
13.30-14.00
Data Preparation and Challenges in Computational Pathology Discusses data preparation steps (digitization, annotation, cleaning) and challenges such as variability in staining, dataset biases, and annotation requirements. Explain augmentation v. real-world variation.
14.00-14.30
Building and Training Models in Computational Pathology Fully-supervised, MIL, unsupervised training approaches
14.30-15.00
Validation and Regulation of Computational Pathology Tools Explore the requirements for validating computational tools and navigating regulatory approval processes like FDA and CE marking.
15.00-15.20
Coffee Break (20 mins)
15.20-15.50
Pathology is a data-heavy science and medical discipline Discuss the volumes of data compared to any other medical discipline, and the importance of expert domain knowledge. Why pathologists must master data analysis techniques.
15.50 - 16.20
Emerging Trends and Future Technical Directions in Computational Pathology Highlight cutting-edge research and innovations, such as multimodal AI, chatbots, and the pivotal role of pathologists in advancing the field.
16.20 - 16.50
Case Studies: Computational Pathology in Action Present real-world case studies of how computational pathology has been applied in diagnostics, research, and prognostic analysis.
16.50 - 17.20
Ethics and the Future of Computational Pathology Discuss ethical considerations, patient data privacy, medical responsibility, and training pathologists in a future where many diagnoses will be delegated to computers.
17.20 - 17.50
Open Discussion and Q&A An open forum for participants to discuss key takeaways, ask questions, and share their thoughts on computational pathology.
17.50 - 18.00
Closing Remarks
28
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