---
license: mit
language:
- en
library_name: pytorch
base_model:
- microsoft/BiomedCLIP-PubMedBERT_256-vit_base_patch16_224
- torchvision/densenet169
datasets:
- kaggle-brain-stroke-ct
tags:
- medical-imaging
- brain-ct
- stroke-classification
- binary-classification
- pytorch
---

    # Ischemic Stroke Binary Classifier

    PyTorch checkpoint artifacts for the MultiAgentMedClassifier stroke task.
    Contains a DenseNet169 CNN checkpoint and a BiomedCLIP linear-probe checkpoint
    for classifying brain CT images as normal or ischemic stroke.

    These are checkpoint files for the accompanying project loaders, not standalone
    Transformers models.

    ## Model Description

    - Task: binary stroke CT classification
    - CNN architecture: DenseNet169
    - Vision-language backbone for probe: `microsoft/BiomedCLIP-PubMedBERT_256-vit_base_patch16_224`
    - Framework: PyTorch

    ## Classes

    - `normal`
    - `stroke`

    The project-level BiomedCLIP labels are:

    - `normal brain CT`
    - `ischemic stroke brain CT`

    ## Files

    - `stroke/cnn/densenet169_CT_stroke_binary_norm_final.pt`: DenseNet169 CNN checkpoint for binary stroke CT classification.
- `stroke/biomedclip/linear_probe_BiomedCLIP_CT_stroke_binary_norm_best.pt`: BiomedCLIP linear-probe checkpoint for binary stroke CT classification.

    ## Training Details

    - Input size: 224 x 224 RGB
    - Normalization: ImageNet mean/std
    - CNN checkpoint: DenseNet169 fine-tuned for the `stroke` task
    - BiomedCLIP probe: linear/MLP probe over frozen BiomedCLIP image features (layer 6)

    ## Metrics

    | Model | Accuracy |
    |-------|----------|
    | DenseNet169 CNN | 97.7% |

    ## Inference Example

    ```python
    from huggingface_hub import hf_hub_download
    from agents.cnn_tool import CNNClassifier
    from config import DEFAULT_CONFIG

    checkpoint_path = hf_hub_download(
        repo_id="tamara-kostova/multiagentmed-stroke",
        filename="stroke/cnn/densenet169_CT_stroke_binary_norm_final.pt",
    )
    DEFAULT_CONFIG.model.cnn_checkpoints["stroke"] = checkpoint_path
    classifier = CNNClassifier(DEFAULT_CONFIG.model, DEFAULT_CONFIG.preprocess)
    result = classifier.classify("path/to/brain_ct.png", task="stroke")
    print(result)
    ```

    ## Intended Use

    Research and experimentation only. Not a medical device. Always validate on your
    own held-out test set before using in any pipeline.