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--- |
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license: cc-by-nc-4.0 |
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language: |
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- en |
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library_name: pytorch |
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pipeline_tag: image-classification |
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metrics: |
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- accuracy |
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- auc |
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- f1 |
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--- |
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# DigeApplication |
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`DigeApplication` is an open-source **tool-model zoo** for downstream gastrointestinal computational pathology tasks, providing unified configs and inference utilities for rapid deployment and reproducible evaluation. |
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- Foundation model: **Digepath** (ViT-L/16, DINOv2 pretraining) |
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- Digepath paper (arXiv): https://arxiv.org/abs/2505.21928 |
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## What is DigeApplication? |
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`DigeApplication` packages a collection of **task-specific tool-models** built on top of gastrointestinal pathology foundation model (Digepath). It aims to provide: |
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- **Model Zoo**: standardized, ready-to-use downstream tool-models |
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- **Unified Configs**: each tool is described by a lightweight `config.json` |
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- **Unified Inference**: consistent API to load and run a tool-model |
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## Repository Structure |
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```text |
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DigeApplication/ |
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├── Model_Zoo/ |
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│ ├── <tool_name_1>/ |
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│ │ ├── config.json |
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│ │ ├── model.safetensors |
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│ └── <tool_name_2>/ |
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├── Model_Factory/ |
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│ ├── digepath_model_factory.py |
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│ └── digepath_modeling_wsi.py |
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│ └── digepath_modeling_roi.py |
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├── DigeApplication.py |
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└── README.md |
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``` |
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## Usage Example |
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```python |
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from Model_Factory.digepath_model_factory import DigepathModelFactory |
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import os |
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import torch |
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model_zoo_dir = "./DigeApplication/Model_Zoo/" |
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device = "cuda:0" |
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dummy_num_patches = 100 |
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model_dirs = [ |
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os.path.join(model_zoo_dir, d) |
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for d in os.listdir(model_zoo_dir) |
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if os.path.isdir(os.path.join(model_zoo_dir, d)) |
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] |
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for digemodel_dir in model_dirs: |
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print(f"Loading model from {digemodel_dir}...") |
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model_factory = DigepathModelFactory(digemodel_dir, device=device) |
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if model_factory.data_type == "ROI": |
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dummy_input = torch.randn(1, 1024, device=device) |
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elif model_factory.data_type == "WSI": |
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dummy_input = torch.randn(1, dummy_num_patches, 1024, device=device) |
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else: |
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raise ValueError(f"Unknown data_type: {model_factory.data_type}") |
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output = model_factory.infer(dummy_input) |
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print(f"Model output: {output}\n") |
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``` |
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## Supported Tasks (Model Zoo) |
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Each tool-model in `DigeApplication` is described by a config containing fields such as: |
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`tool_name`, `data_type`, `task_type`, `organ_domain`, `num_classes` |
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| Tool / Model Name | Task Type | Data Type | Organ Domain | Classes | License | |
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|:--------------------------|:---------:|:---------:|:------------:|--------:|:-------------:| |
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| BOW_CAMEL_2CLS_ROI | CLS | ROI | BOW | 2 | cc_by_nc_4.0 | |
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| BOW_CENPL_5CLS_ROI | CLS | WSI | BOW | 5 | cc_by_nc_4.0 | |
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| BOW_COAD-MUC16_2CLS_WSI | CLS | WSI | BOW | 2 | cc_by_nc_4.0 | |
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| BOW_COAD-TTN_2CLS_WSI | CLS | WSI | BOW | 2 | cc_by_nc_4.0 | |
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| BOW_CRC100K_9CLS_ROI | CLS | ROI | BOW | 9 | cc_by_nc_4.0 | |
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| BOW_READ-NRAS_2CLS_WSI | CLS | WSI | BOW | 2 | cc_by_nc_4.0 | |
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| BOW_READ-TTN_2CLS_WSI | CLS | WSI | BOW | 2 | cc_by_nc_4.0 | |
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| BOW_TNM_4CLS_WSI | CLS | WSI | BOW | 4 | cc_by_nc_4.0 | |
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| DIGE_HER2_2CLS_WSI | CLS | WSI | DIGE | 2 | cc_by_nc_4.0 | |
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| DIGE_IMPCRS_3CLS_ROI | CLS | WSI | DIGE | 3 | cc_by_nc_4.0 | |
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| DIGE_MSI_2CLS_WSI | CLS | WSI | DIGE | 2 | cc_by_nc_4.0 | |
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| DIGE_P53_2CLS_WSI | CLS | WSI | DIGE | 2 | cc_by_nc_4.0 | |
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| DIGE_PDL1_2CLS_WSI | CLS | WSI | DIGE | 2 | cc_by_nc_4.0 | |
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| DIGE_POORAS_2CLS_WSI | CLS | WSI | DIGE | 2 | cc_by_nc_4.0 | |
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| DIGE_TUM_LHN_3CLS_WSI | CLS | WSI | DIGE | 3 | cc_by_nc_4.0 | |
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| DIGE_UNITOPATHO_6CLS_ROI | CLS | ROI | DIGE | 6 | cc_by_nc_4.0 | |
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| ESO_ADESQUA_2CLS_WSI | CLS | WSI | ESO | 2 | cc_by_nc_4.0 | |
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| ESO_EPILES_2CLS_WSI | CLS | WSI | ESO | 2 | cc_by_nc_4.0 | |
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| ESO_EPILES_4CLS_WSI | CLS | WSI | ESO | 4 | cc_by_nc_4.0 | |
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| ESO_KERATIN_2CLS_WSI | CLS | WSI | ESO | 2 | cc_by_nc_4.0 | |
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| GAST_ACT_3CLS_WSI | CLS | WSI | GAST | 3 | cc_by_nc_4.0 | |
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| GAST_ATR_2CLS_WSI | CLS | WSI | GAST | 2 | cc_by_nc_4.0 | |
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| GAST_GASTRITIS_3CLS_WSI | CLS | WSI | GAST | 3 | cc_by_nc_4.0 | |
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| GAST_IM_4CLS_WSI | CLS | WSI | GAST | 4 | cc_by_nc_4.0 | |
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| GAST_STLC_11CLS_ROI | CLS | ROI | GAST | 11 | cc_by_nc_4.0 | |
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| GAST_TNM_4CLS_WSI | CLS | WSI | GAST | 4 | cc_by_nc_4.0 | |
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| GAST_XANSRC_2CLS_WSI | CLS | WSI | GAST | 2 | cc_by_nc_4.0 | |
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## Citation |
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If `DigeApplication` is helpful to you, please cite our work. |
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``` |
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@article{zhu2025subspecialty, |
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title={Subspecialty-specific foundation model for intelligent gastrointestinal pathology}, |
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author={Zhu, Lianghui and Ling, Xitong and Ouyang, Minxi and Liu, Xiaoping and Guan, Tian and Fu, Mingxi and Cheng, Zhiqiang and Fu, Fanglei and Zeng, Maomao and Liu, Liming and others}, |
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journal={arXiv preprint arXiv:2505.21928}, |
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year={2025} |
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} |
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``` |
