Digital Pathology
Empowering Diagnostics Through Digital Innovation
Skills you will gain:
Digital pathology represents a major shift in diagnostic medicine, offering improved efficiency, scalability, and accessibility. This course begins by examining the transition from analog to digital pathology, exploring critical components such as slide scanners, image acquisition systems, and integration with Laboratory Information Management Systems (LIMS). Participants will gain insight into telepathology and the development of automated pipelines for diagnostic workflows, leveraging artificial intelligence and metadata-driven image analysis.
Building upon this foundation, the course delves into various imaging modalities—brightfield, fluorescence, and multispectral imaging—while emphasizing image quality control and reproducibility. It also covers computational processing techniques like tissue segmentation and feature extraction. The final segment focuses on interpreting digital pathology data using advanced computational techniques, including scoring algorithms and semantic markup for diagnostics. This program is ideal for learners aiming to be at the forefront of the digital healthcare revolution.
Aim: This course aims to provide learners with a comprehensive understanding of the digital transformation in pathology. From slide digitization and imaging standards to computational analysis and diagnostic interpretation, the program is designed to equip participants with the tools and frameworks essential for modern pathological workflows. It bridges the gap between laboratory practices and technological innovation, emphasizing quality, automation, and digital integration for scalable and AI-driven healthcare systems.
Program Objectives:
- Understand the components and workflows of digital pathology systems
- Evaluate and compare various imaging modalities used in pathology
- Apply quality assurance protocols to ensure reproducibility and standardization
- Perform computational image processing for tissue segmentation and feature analysis
- Interpret digital pathology data using advanced computational frameworks
What you will learn?
Week 1: Digital Systems and Workflow Architecture
- Transition from analog to digital pathology platforms
- Components of digital imaging and slide scanners
- Integration with LIMS
- Telepathology and automated diagnostic pipelines
Week 2: Imaging Modalities and Quality Protocols
- Brightfield, fluorescence, and multispectral imaging comparison
- Optical and computational factors in image quality
- Standards for image reproducibility in labs
- Global QA practices for imaging validation
Week 3: Computational Image Processing
- Resolution enhancement and artifact reduction
- Intensity normalization and color consistency
- Tissue segmentation via algorithms
- Feature extraction: morphological and textural
Week 4: Machine Intelligence in Pathology Interpretation
- CNNs and ensemble models in image analysis
- AI model deployment for scoring and classification
- Multi-class classification and diagnostic modeling
- Evaluation metrics, ethics, and translational issues
Intended For :
- Students pursuing or holding a degree in Pathology, Biomedical Engineering, Biotechnology, Computer Science, or related fields.
- PhD Scholars and Researchers working in digital pathology, AI in healthcare, or biomedical image analysis.
- Industry Professionals from healthcare, bioinformatics, pharmaceutical R&D, diagnostics, or medical imaging sectors.
- Aspiring Learners interested in applying AI, machine learning, and digital health tools in pathology and diagnostics.
Career Supporting Skills