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将生物大模型、对接计算引擎与组学管线并排横向对比。按获取方式、硬件开销及细分领域灵活筛选，在一分钟内得出客观可辩护的技术选型方案。
| 技术参数指标 | ＋ 添加工具 | |
|---|---|---|
| Category | Research Imaging & Microscopy | — |
| Subcategory | Digital Pathology & Whole-Slide Analysis | — |
| Company | University of Edinburgh | — |
| Access mode | Open Source | — |
| Licence conditions | GPL-3.0 | — |
| Compute requirements | Standard desktop / workstation (multi-core CPU, optional GPU for deep learning) | — |
| Biological application | Oncology biomarker quantification, spatial histology analysis, and clinical trials | — |
| Research workflow | Whole-slide image -> cell segmentation -> phenotyping -> spatial biomarker scoring | — |
| Evidence level | Peer-reviewed research (Scientific Reports 2017) with thousands of citations | — |
| Commercial eligibility | Permissive open source release | — |
| Price | Free Open Source | — |
| Availability ⓘ | Available for Windows, macOS, and Linux | — |
| Features | High-performance whole-slide image viewing and annotation across gigapixel files, Machine learning tissue classification (tumor vs stroma vs necrosis), Integration with StarDist and Cellpose for deep learning cell and nuclear segmentation, Extensive scripting API in Groovy and Python for automated cohort analysis | — |
| Entry type | Open Source Software | — |
| AI role | Deep Learning Image Segmentation | — |
| Input data | 未明确记录 | — |
| Output data | 未明确记录 | — |
| Validation | 未明确记录 | — |
| Type | Digital pathology bioimage analysis platform | — |
| Intended use | 未明确记录 | — |
| Compatibility | 未明确记录 | — |
| Manufacturer | University of Edinburgh (Bankhead Lab) | — |
| Integration evidence | https://qupath.github.io | — |
| Laboratory handoff | Correlates histological features with genomic data and patient survival curves | — |