Technology
Powered by PathGPT — DiPath's AI engine for digital pathology workflows
PathGPT Multimodal Engine
- Image-level classification and structured scoring workflows
- Multimodal analysis across image, region, and report context
Enlarged PathGPT workflow view with higher-contrast framing for clearer reasoning-path visualization.
AI-Assisted Analysis Capabilities
Configured workflows can support region suggestions, structured scoring, and quantitative outputs for user review. Availability and intended use vary by product, version, and market.
Region-of-Interest Suggestions
Highlights candidate regions for user review; relevant areas must be confirmed by the qualified user.
Structured Scoring Support
Presents configured measurements and grading outputs to support review; results require verification against the applicable workflow.
Contextual Pattern Analysis
Analyzes supported image features and presents outputs for review; it does not replace clinical judgment or product-specific intended-use guidance.
Cellular Quantification
Signal Intensity Histograms
Presents signal-distribution summaries and configurable quantitative outputs for user review.
AI-Powered Cellular Segmentation
Integration & Workflow
A high-level view of upload, analysis, review, and report preparation; implementation details vary by product and deployment.
Slide Upload
Digital slides uploaded to the platform
AI Analysis
PathGPT processes and analyzes
Quality Control
Automated validation checks
Results
Report draft prepared
Doctor Review
Expert validation and approval
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
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)
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)
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)
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)
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