Programme Parameters
Advanced Postgrad
3 Days (4.5 Hrs)
Online (e-LMS)
• Prior working knowledge of the field and comfort with core tools and quantitative reasoning.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
Digital pathology has revolutionized diagnostic workflows by converting histological slides into high-resolution digital images. The integration of AI allows pathologists to analyze large datasets with increased speed, precision, and reproducibility. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
Digital pathology has revolutionized diagnostic workflows by converting histological slides into high-resolution digital images. The integration of AI allows pathologists to analyze large datasets with increased speed, precision, and reproducibility.
📋 Course Objectives
1. Put biotechnology techniques to work on real datasets and case studies.
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
• A portfolio-grade biotechnology deliverable you can defend and extend.
• 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
Introduction to Digital Pathology and AI
Understand digital pathology workflows and challenges. • Explore the role of AI in transforming diagnostics. • Identify key areas of AI application in pathology.
Fundamentals of Convolutional Neural Networks (CNNs)
Grasp CNN architecture, including convolution, pooling, and activation layers. • Address pathology-specific challenges like stain variation and magnification levels. • Implement data preparation techniques such as WSI patching and color normalization.
Image Preprocessing and Feature Extraction
Learn image preprocessing, annotation, and feature extraction techniques specific to pathology. • Apply data augmentation strategies to enhance model robustness. • Prepare digital pathology images for AI model input.
Building and Training Deep Learning Models
Choose appropriate CNN architectures like ResNet, VGG, DenseNet, or EfficientNet. • Implement dataset splitting and validation methods. • Handle class imbalance and select effective evaluation metrics. • Train a CNN model for tissue classification in a hands-on session.
Model Optimization and Transfer Learning
Perform hyperparameter tuning and apply regularization methods. • Utilize early stopping and learning rate scheduling for efficient training. • Implement transfer learning with pre-trained models and fine-tuning for pathology. • Interpret model decisions using techniques like Grad-CAM.
Model Validation and Real-World Applications
Evaluate and validate AI models for clinical relevance and accuracy. • Apply developed AI models to real-world pathology datasets and case studies. • Gain insights into improving diagnostic accuracy and personalized patient care.
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | Algorithms |
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