Deep Learning for Histopathology Image Analysis
Transforming Histopathology Analysis with Deep Learning for Faster, More Accurate Diagnoses
About This Course
Histopathology image analysis plays a critical role in diagnosing various diseases, including cancers, neurological disorders, and inflammatory conditions. With the advent of deep learning, there has been a significant shift toward automating the analysis of these images, enhancing diagnostic speed, accuracy, and reproducibility.
This workshop will cover the use of convolutional neural networks (CNNs), transfer learning, and other deep learning techniques to extract meaningful insights from histopathology images. Participants will gain hands-on experience working with real-world datasets to train and evaluate AI models, focusing on tissue classification, detection of cancerous cells, and disease progression tracking. Case studies on the clinical applications of AI in histopathology will also be shared.
Aim
This workshop aims to explore the application of deep learning techniques to histopathology image analysis, focusing on improving diagnostic accuracy and automating tasks in pathology. Participants will learn how AI can be used for automated tissue segmentation, classification of cancerous tissues, and identifying disease markers from high-resolution histopathological images.
Workshop Objectives
- Understand the basics of histopathology and the significance of histopathology images in diagnostics.
- Learn the fundamentals of deep learning, focusing on convolutional neural networks (CNNs) and other architectures for image analysis.
- Gain hands-on experience in using deep learning techniques to analyze histopathology images and classify tissues.
- Apply transfer learning for effective model training on limited datasets in histopathology image analysis.
- Analyze real-world case studies of AI models applied to histopathology image classification and cancer detection.
Workshop Structure
Day 1 – Introduction to Histopathology Imaging and AI Workflows
- Understanding the Role of Histopathology Imaging in Medical Diagnostics
- Exploring the Use of Deep Learning and AI in Pathology Image Analysis
- Preparing Histopathology Data: Preprocessing, Augmentation, and Normalization
- Techniques for Image Segmentation and Annotation in Pathology Workflows
Day 2 – Building Deep Learning Models for Tumor Detection
- Choosing the Right Deep Learning Architectures for Tumor Detection in Pathology
- Effective Dataset Splitting and Validation Methods for Medical Image Models
- Addressing Class Imbalance in Medical Datasets and Selecting Evaluation Metrics
- Hands-on Session: Applying U-Net or Mask R-CNN for Tumor Detection and Segmentation
Day 3 – Model Optimization and Transfer Learning
- Optimizing Deep Learning Models: Hyperparameter Tuning and Regularization Techniques
- Using Early Stopping and Learning Rate Scheduling for Model Efficiency
- Transfer Learning: Fine-Tuning Pre-Trained Models for Pathology-Specific Tasks
- Hands-on Session: Implementing Transfer Learning and Model Interpretability Techniques (e.g., Grad-CAM)
Who Should Enrol?
- Undergraduate/Postgraduate Degree in Computer Science, Biomedical Engineering, Biotechnology, Data Science, or related fields.
- Professionals in healthcare, pathology, medical imaging, or AI/ML research.
- Data Scientists and AI Engineers interested in applying deep learning techniques to healthcare and medical imaging.
- Individuals with a strong interest in the intersection of AI and healthcare for disease diagnosis and medical imaging.
Important Dates
Registration Ends
11/03/2025
IST 7:00 PM
Workshop Dates
11/03/2025 – 11/05/2025
IST 8:00 PM
Workshop Outcomes
- Ability to understand the role of deep learning in histopathology image analysis for disease diagnosis.
- Proficiency in applying deep learning models to segment, classify, and interpret histopathology images.
- Hands-on experience in using real-world datasets for model training, evaluation, and enhancement.
- Skills in using transfer learning techniques to improve model performance with limited data.
- Ability to analyze and interpret AI results for clinical application in medical diagnostics.
- Familiarity with the challenges and considerations in implementing AI in real-world histopathology workflows.
Fee Structure
Student Fee
₹1499 | $55
Ph.D. Scholar / Researcher Fee
₹2499 | $65
Academician / Faculty Fee
₹3499 | $80
Industry Professional Fee
₹4499 | $90
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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