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Biotechnology · Moderate · Video-based learning

Deep Learning for Histopathology Image Analysis

Transforming Histopathology Analysis with Deep Learning for Faster, More Accurate Diagnoses

Access 24/7 · 365 daysVideo lectures of 3+ hrsCertificate
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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. Convolutional neural networks and modern AI architectures now play a central role in tumor detection, grading, and outcome prediction.

This workshop provides a structured introduction to histopathology image analysis using deep learning, covering image preprocessing, patch-based learning, classification, and model evaluation. Through hands-on dry-lab sessions, participants will work with real histopathology datasets and learn best practices for performance assessment and explainability, preparing them for research or industry roles in digital pathology.

Aim

This workshop aims to train participants in applying deep learning techniques to histopathology images for disease detection, classification, and biomarker analysis. It focuses on computational (dry-lab) workflows for handling large pathology images, feature extraction, and model development. Participants will gain practical skills in building, evaluating, and interpreting deep learning models for medical image analysis. The program bridges AI research with real-world digital pathology applications.

Next: Syllabus →

Syllabus

Module 1 – Introduction to Histopathology Imaging and AI Workflows

  • Introduction to Histopathology Imaging and AI Workflows
  • Data Preprocessing, Augmentation & Normalization
  • Image Segmentation and Annotation Techniques

Module 2 – Building Deep Learning Models for Tumor Detection

  • Deep Learning Architectures for Tumor Detection
  • Dataset Splitting, Validation & Evaluation Metrics
  • Hands-on: Applying U-Net / Mask R-CNN for Segmentation

Module 3 – Model Optimization and Transfer Learning

  • Model Optimization & Transfer Learning
  • Hyperparameter Tuning & Regularization Techniques
  • Hands-on: Grad-CAM for Model Interpretability

Who Should Enrol?

  • Doctoral Scholars & Researchers: PhD candidates seeking to integrate computational workflows into their molecular research.
  • Postdoctoral Fellows: Early-career scientists aiming to enhance their data-driven publication profile.
  • University Faculty: Professors and HODs interested in modern bioinformatics pedagogy and tool mastery.
  • Industry Scientists: R&D professionals from the Biotechnology and Pharmaceutical sectors transitioning to genomic-driven discovery.
  • Postgraduate Students: Final-year PG students looking for specialized research-grade exposure beyond standard curricula.
Next: Fees →

Course fee

Learners in India

₹2,499

Learners outside India

$50

  • Video lectures
  • Study material
  • Certificate after completion
  • 365-day access

Enrol in the course

The recordings are being uploaded. They appear in your dashboard as soon as they are ready, and we will e-mail you then.

Next: FAQ →

Frequently asked questions

How is this course taught?

Through recorded video lectures from the original live workshop, with the study material. Watch at your own pace, any time.

How long do I have access?

365 days from your registration, on your NanoSchool dashboard.

Do I get a certificate?

Yes, an e-certificate after completion, with a QR code anyone can verify and an "Add to LinkedIn" button.

How can I pay?

By card, UPI or PayPal on the payment page; international bank transfer is also available there.

What is the refund policy?

See our Returns, Refunds & Cancellation policy: Returns, Refunds & Cancellation policy

Need Help?

We’re here for you!


(+91) 120-4781-217
WhatsApp +91 99581 61117

Registration open

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