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
Histopathology remains the gold standard for disease diagnosis, especially in cancer. With the digitization of pathology slides, deep learning has emerged as a powerful tool to analyze tissue morphology at scale, enabling reproducible and objective decision support for pathologists. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
Histopathology remains the gold standard for disease diagnosis, especially in cancer. With the digitization of pathology slides, deep learning has emerged as a powerful tool to analyze tissue morphology at scale, enabling reproducible and objective decision support for pathologists.
📋 Course Objectives
1. Put biotechnology techniques to work on real datasets and case studies.
2. Produce a reproducible, portfolio-ready project you can cite in a thesis, paper, or job application.
👥 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
Foundations of Histopathology Imaging and AI
Grasp fundamental concepts of histopathology imaging workflows. • Explore the role of AI in digital pathology. • Understand the digitization process of pathology slides.
Data Preparation and Preprocessing for Deep Learning
Implement essential data preprocessing techniques. • Apply image augmentation for robust model training. • Perform image normalization for consistent dataset characteristics.
Image Segmentation and Annotation
Master various image segmentation techniques. • Learn advanced annotation strategies for histopathology images. • Identify key structures within complex tissue samples.
Deep Learning Architectures for Tumor Detection
Explore state-of-the-art deep learning models for tumor detection. • Understand the principles of Convolutional Neural Networks (CNNs). • Apply patch-based learning for detailed image analysis.
Model Training, Validation, and Evaluation
Implement effective dataset splitting and validation strategies. • Evaluate model performance using appropriate metrics. • Gain practical experience with U-Net and Mask R-CNN for segmentation.
Model Optimization, Transfer Learning, and Interpretability
Optimize deep learning models for improved accuracy and efficiency. • Apply transfer learning techniques to new histopathology tasks. • Utilize Grad-CAM for model interpretability and explainability.
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | Keras |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | OpenCV |
| Covered Tool / Platform | scikit-image |
| Covered Tool / Platform | U-Net |
| Covered Tool / Platform | Mask R-CNN |
| Covered Tool / Platform | Grad-CAM |
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