| --- |
| license: cc-by-nc-4.0 |
| base_model: |
| - Qwen/Qwen3.5-9B |
| - autotrust/JEV-9B |
| - autotrust/JEV-27B-VL |
| language: |
| - en |
| tags: |
| - pathology |
| - histopathology |
| - whole-slide-image |
| - vision-language |
| - system-one |
| - medical |
| extra_gated_prompt: >- |
| PathoJev is released for non-commercial research use under CC BY-NC 4.0. It is not a medical device and must not be |
| used for diagnosis or clinical decision-making. Access requests are reviewed manually. |
| extra_gated_fields: |
| Full name: text |
| Affiliation: text |
| Country: country |
| Intended use: text |
| I will use PathoJev for non-commercial research only: checkbox |
| I will not use PathoJev for clinical decision-making: checkbox |
| --- |
| |
| # PathoJev |
|
|
| PathoJev answers pathology questions directly as probabilities over the answer options, for whole-slide images |
| (WSIs) and regions of interest (ROIs). It builds on the System One model JEV with a Qwen3.5 hybrid backbone. A WSI |
| enters as a 32 x 32 grid of pooled CONCH v1.5 patch features and is encoded once; every question is then read out as an |
| isolated row on the shared slide context by a typed decision head, without generating text. ROI images go through the |
| backbone's own vision tower. |
|
|
| Code: https://github.com/ChyaZhang/PathoJev |
|
|
| ## Files |
|
|
| | Path | Content | |
| |---|---| |
| | `9b/` | PathoJev-9B: LoRA adapter, decision head and slide projection (`pathojev.safetensors`) and `config.json`; backbone [Qwen/Qwen3.5-9B](https://huggingface.co/Qwen/Qwen3.5-9B) | |
| | `27b/` | PathoJev-27B, same layout; backbone [autotrust/JEV-27B-VL](https://huggingface.co/autotrust/JEV-27B-VL) | |
| | `pathojev/`, `demo.py` | minimal inference code (same as the GitHub repository) | |
|
|
| Access is gated: request it on this page, then log in with `hf auth login`. The backbone is downloaded on first use. PathoJev-9B needs about 20 GB of GPU memory and PathoJev-27B about 60 GB. |
|
|
| ## Usage |
|
|
| ```bash |
| hf download Mikezcy/PathoJev --exclude "*.safetensors" --local-dir PathoJev && cd PathoJev |
| pip install -r requirements.txt |
| ``` |
|
|
| ```python |
| from pathojev import PathoJev, make_grid |
| |
| model = PathoJev.from_pretrained("9b") # or "27b"; weights are fetched from this repository |
| grid = make_grid("slide.h5") # TRIDENT CONCH v1.5 features (20x, 512 x 512 px patches) |
| questions = [ |
| {"question": "What is the histologic grade of the tumor?", "options": ["Low-grade", "High-grade"]}, |
| {"question": "Lymphovascular invasion is present.", "kind": "noul"}, # true / false statement |
| ] |
| print(model.answer_slide(grid, questions)) # [{"answer": ..., "probs": {option: probability}}, ...] |
| ``` |
|
|
| `model.answer_roi(images, questions)` answers one question per ROI image. `demo.py` runs the eight SlideBench-TCGA |
| questions of slide TCGA-XF-AAMJ; see the GitHub repository for the expected output. |
|
|
| ## Results |
|
|
| Accuracy (%): |
|
|
| | Model | WSI-VQA | WSI-Bench | SlideBench-TCGA | PathMMU-tiny | PathVQA | Quilt-VQA | OmniMedVQA | MedXpertQA | |
| |---|---:|---:|---:|---:|---:|---:|---:|---:| |
| | PathoJev-9B | 65.4 | 90.4 | 75.9 | 71.9 | 94.6 | 70.8 | 75.3 | 25.6 | |
| | PathoJev-27B | 66.4 | 91.1 | 76.9 | 73.8 | 95.1 | 67.3 | 69.9 | 50.0 | |
|
|
| ## License |
|
|
| CC BY-NC 4.0, for non-commercial research use. CONCH v1.5 and pre-extracted TCGA features have their own licenses. |
| PathoJev is not a medical device and must not be used for clinical decision-making. |
|
|
