Programme Parameters
Foundation
3 Days (4.5 Hrs)
Online (e-LMS)
• No prior experience required — basic computer literacy is sufficient.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
Digital histopathology has rapidly evolved with the integration of artificial intelligence, enabling scalable and reproducible analysis of whole-slide images. Traditional pixel-level annotations are costly and time-consuming, making modern approaches like MIL and transformers essential for learning directly from slide-level labels. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
Digital histopathology has rapidly evolved with the integration of artificial intelligence, enabling scalable and reproducible analysis of whole-slide images. Traditional pixel-level annotations are costly and time-consuming, making modern approaches like MIL and transformers essential for learning directly from slide-level labels.
📋 Course Objectives
1. Translate biotechnology theory into practical, reproducible analysis.
2. Assemble a documented case study that evidences your applied capability.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in biotechnology
• R&D engineers and working professionals applying biotechnology in industry
• Academics and educators building research or teaching capacity in biotechnology
🚀 Key Learning Outcomes
• Tangible, reproducible biotechnology work to show supervisors or employers.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Whole-Slide Imaging Fundamentals
• WSI formats, pyramidal storage and reading tiles efficiently
• Stain variation across scanners and laboratories, and normalisation methods
• Tissue detection, artefact rejection and quality control at scale
Tile-Level Modelling
• Magnification selection and the context-versus-detail trade-off
• Augmentation appropriate to histology, including stain augmentation
• Self-supervised pretraining on unlabelled slides
Multiple Instance Learning for Slide-Level Labels
• The weak-label problem: one diagnosis, thousands of tiles
• Attention-based MIL and instance aggregation strategies
• Interpreting attention heatmaps without overclaiming localisation
Context Across the Slide
• Vision transformers on histology tiles
• Modelling spatial relationships between regions rather than tiles in isolation
• Foundation models for pathology and how to evaluate their transfer
Validation and Deployment in the Laboratory
• Multi-site validation and scanner generalisation failure
• Regulatory expectations for computational pathology tools
• Integration with the laboratory information system and pathologist workflow
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | Keras |
| Covered Tool / Platform | CUDA |
| Covered Tool / Platform | Jupyter Notebook |
| Covered Tool / Platform | Weights & Biases |
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