| --- |
| license: cc-by-nc-4.0 |
| language: |
| - en |
| pipeline_tag: image-feature-extraction |
| library_name: timm |
| metrics: |
| - accuracy |
| --- |
| # Model Card for Digepath |
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| <!-- Provide a quick summary of what the model is/does. --> |
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| `Digepath` is a self-supervised foundation model for intelligent gastrointestinal pathology images analysis. Arxiv preprint paper: [https://arxiv.org/abs/2505.21928] |
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| The model is a Vision Transformer Large/16 with DINO-V2 [1] self-supervised pre-training on 353 million multi-scale images from 210,043 H&E-stained gastrointestinal related slides. |
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| ## Introduction of Digepath |
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| Gastrointestinal (GI) diseases represent a clinically significant burden, necessitating precise diagnostic approaches to optimize patient outcomes. Conventional histopathological diagnosis suffers from limited reproducibility and diagnostic variability. To overcome these limitations, we develop Digepath, a specialized foundation model for GI pathology. Our framework introduces a dual-phase iterative optimization strategy combining pretraining with fine-screening, specifically designed to address the detection of sparsely distributed lesion areas in whole-slide images. Digepath was initially pretrained on a large-scale dataset comprising over _**353**_ million multi-scale images derived from _**210,043**_ H&E-stained slides of GI diseases. It was subsequently fine-tuned on _**471,443**_ carefully selected regions of interest (ROIs) in the second stage. It attains state-of-the-art performance on 32 out of 33 tasks related to GI pathology, including pathological diagnosis, protein expression status prediction, gene mutation prediction, and prognosis evaluation. _**Digepath**_ demonstrates broad applicability across diverse clinical tasks, highlighting its potential for reliable deployment in real-world pathology workflows. |
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| ## Using Digepath to extract features from gastrointestinal pathology image |
|
|
| ```python |
| import timm |
| import torch |
| import torchvision.transforms as transforms |
| |
| model = timm.create_model('hf_hub:xtxx/Digepath', pretrained=True, init_values=1e-5, dynamic_img_size=True) |
| |
| preprocess = transforms.Compose([ |
| transforms.Resize(224), |
| transforms.ToTensor(), |
| transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),]) |
| |
| model = model.to('cuda') |
| model.eval() |
| |
| input = torch.randn([1, 3, 224, 224]).cuda() |
| |
| with torch.no_grad(): |
| output = model(input) # [1, 1024] |
| ``` |
|
|
| ## Training Pipeline |
| - Self Supervised Learning: https://github.com/facebookresearch/dinov2 |
|
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| ## Evaluation Pipeline |
| - WSI Classification: https://github.com/lingxitong/MIL_BASELINE |
| - ROI Classification: https://github.com/lingxitong/HistoROIBench |
| - ROI Segmentation: https://github.com/lingxitong/PFM_Segmentation |
|
|
| ## Citation |
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| If `Digepath` is helpful to you, please cite our work. |
|
|
| ``` |
| @article{zhu2025subspecialty, |
| title={Subspecialty-specific foundation model for intelligent gastrointestinal pathology}, |
| 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}, |
| journal={arXiv preprint arXiv:2505.21928}, |
| year={2025} |
| } |
| ``` |
|
|
| ## References |
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| [1] Oquab, Maxime, et al. "Dinov2: Learning robust visual features without supervision." arXiv preprint arXiv:2304.07193 (2023). |
