Comprehensive resources on digital pathology implementation, whole slide imaging, AI-assisted diagnosis, computational pathology and the technologies transforming laboratory medicine.
The acquisition, management, sharing and interpretation of pathology information — including slides, images and data — in a digital environment.
A comprehensive introduction to digital pathology — how glass slides are digitized, viewed, analysed and shared electronically.
Understanding whole slide scanners, image acquisition, resolution standards, and quality control for diagnostic use.
Comparative review of leading whole slide imaging platforms — specifications, speed, throughput and clinical validation.
Enterprise image management, storage solutions, DICOM for pathology, and integration with laboratory information systems.
End-to-end digital workflow design — from accessioning through scanning, diagnosis, reporting and archival.
Remote diagnostic pathology — frozen section consultation, second opinions, and cross-border telepathology services.
Strategic planning for laboratory digital transformation — business case, change management, and phased implementation.
DICOM standard for pathology images — interoperability, metadata, and integration with hospital imaging infrastructure.
Real-world case studies from laboratories that have completed full digital pathology implementation — lessons learned and best practices.
Machine learning, deep learning, foundation models and generative AI — how artificial intelligence is transforming diagnostic pathology.
Comprehensive guide to artificial intelligence applications in pathology — current capabilities, clinical use cases, and adoption trends.
The intersection of computer science and pathology — quantitative tissue analysis, spatial biology, and predictive modelling.
Machine learning fundamentals for pathologists — supervised and unsupervised learning, training data, and model evaluation.
Convolutional neural networks, attention mechanisms, and multi-instance learning for histopathology image analysis.
Pre-trained vision transformers for pathology — self-supervised learning on millions of tissue images for downstream diagnostic tasks.
Large language models and generative AI in pathology — report generation, literature synthesis, and diagnostic support.
Clinical decision support — AI tools for cancer detection, grading, biomarker quantification, and prognosis prediction.
Automated analysis of H&E and IHC stained tissue images — segmentation, classification, and quantitative morphometry.
AI algorithms for detecting cancer in tissue specimens — breast, prostate, lung, colorectal and beyond.
Automated quantification of PD-L1, HER2, Ki-67, hormone receptors, and emerging biomarkers using AI.
Predicting clinical outcomes, treatment response and molecular features directly from tissue morphology using deep learning.
Regulatory frameworks, clinical validation requirements, CE marking, and FDA clearance for AI-based diagnostic tools.
Bias mitigation, explainability, reproducibility, and responsible deployment of AI in clinical pathology practice.
Patient data protection, anonymization standards, consent for AI training, and GDPR compliance in pathology AI research.
Companies, products and resources in the digital pathology and AI ecosystem.
| Product | Type | Scan Speed | Resolution | Capacity | AI Integration |
|---|---|---|---|---|---|
| Scanner A | Whole Slide Scanner | 60 sec / slide | 0.25 μm/pixel | 400 slides | Yes |
| Scanner B | Whole Slide Scanner | 45 sec / slide | 0.25 μm/pixel | 300 slides | Yes |
| Scanner C | Whole Slide Scanner | 90 sec / slide | 0.20 μm/pixel | 200 slides | Limited |
| Platform D | Image Management | N/A | N/A | Unlimited | Yes |
| Software E | AI Analysis | N/A | N/A | N/A | Native |
Specifications are indicative. Contact manufacturers for current product specifications and clinical validation status.