Technology
Powered by PathGPT - Our proprietary AI engine for digital pathology
PathGPT Multimodal Engine
Powered by PathGPT multimodal engine, clinically validated in 500+ SCI papers. Achieves gold-standard robustness (κ>0.92) in histopathology analysis, enabling automated lesion localization, grading, and prognosis prediction with 300% efficiency gain over conventional methods.
- Automated lesion localization with sub-micron precision
- Precision grading for cancer classification
- Prognosis prediction based on histopathological patterns
- Real-time analysis with instant AI activation
SCI papers validated
Gold-standard robustness
Efficiency gain
Enlarged PathGPT workflow view with higher-contrast framing for clearer reasoning-path visualization.
AI-Powered Diagnostic Intelligence
Our AI algorithms perform whole-slide analysis to automatically calculate the percentage of positive cells, distinguish tumor cells from stromal cells, and reduce variability in manual counting.
Automated Lesion Localization
AI automatically identifies and marks regions of interest, reducing manual screening time and ensuring no critical areas are missed.
Precision Grading
Standardized and consistent grading across all samples, eliminating inter-observer variability and ensuring reliable results.
Prognosis Prediction
Machine learning models analyze patterns to predict patient outcomes and guide treatment decisions with data-driven insights.
Real-time Analysis
Instant processing and results delivery, enabling faster decision-making and improved patient care workflows.
Precision Cellular Analytics
Proprietary segmentation algorithm achieves sub-micron precision (Dice>0.95 for nucleus/membrane/cytosol). AI enhancement boosts SNR by 10dB, with signal intensity histograms demonstrating raw vs. processed data comparison.
Sub-micron Precision
Dice coefficient >0.95 for accurate cell boundary detection and segmentation.
AI Enhancement
Signal-to-noise ratio boosted by 10dB through intelligent image enhancement.
Signal Intensity Histograms
Comprehensive quantitative analysis with detailed signal distribution metrics.
AI-Powered Cellular Segmentation
Sub-micron precision in identifying nucleus, membrane, and cytosol structures with Dice coefficient >0.95
Integration & Workflow
Streamlined process from sample to diagnosis, designed for maximum efficiency.
Slide Upload
Digital slides uploaded to the platform
AI Analysis
PathGPT processes and analyzes
Quality Control
Automated validation checks
Results
Comprehensive report generated
Doctor Review
Expert validation and approval
Security & Compliance
Enterprise-grade security with global regulatory certifications.
HIPAA Compliant
Full compliance with healthcare data protection standards
ISO 27001
Information Security Management certified
NMPA Class III / CE IVDR
Medical device registration for global markets
Powerful AI Analysis in Action
Experience the next generation of digital pathology with our intuitive AI-powered interface.
Publications
Selected entries below are copied directly from our Publications page.
ToPoFM: Topology-Guided Pathology Foundation Model for High-Resolution Pathology Image Synthesis with Cellular-Level Control
Li, J., Zhu, C., Zheng, S., Chen, P., Sun, Y., Li, H., Yang, L. · IEEE Trans. Medical Imaging (IF 11.3)
PathAsst: A Generative Foundation AI Assistant towards Artificial General Intelligence of Pathology
Sun, Y., Zhu, C., Zheng, S., Zhang, K., Sun, L., Shui, Z., Zhang, Y., Li, H., Yang, L. · AAAI 2024 (CCF-A, Oral) · 59 cited
PathMMU: A Massive Multimodal Expert-Level Benchmark for Understanding and Reasoning in Pathology
Sun, Y., Wu, H., Zhu, C., Zheng, S., Chen, Q., Zhang, K., Zhang, Y., Wan, D., Lan, X., ... · ECCV 2024 (CCF-A, Oral) · 7 cited
Task-specific Fine-tuning via Variational Information Bottleneck for Weakly-supervised Pathology WSI Classification
Li, H., Zhu, C., Zhang, Y., Sun, Y., Shui, Z., Kuang, W., Zheng, S., Yang, L. · CVPR 2023 (CCF-A) · 75 cited
Pathologist-level Interpretable Whole-slide Cancer Diagnosis with Deep Learning
Zhang, Z., Chen, P., McGough, M., ..., Cui, L., ..., Yang, L. · Nature Machine Intelligence
WSI-VQA: Interpreting Whole Slide Images by Generative Visual Question Answering
Chen, P., Zhu, C., Zheng, S., Li, H., Yang, L. · ECCV 2024 (CCF-A) · 9 cited
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