---
license: mit
language:
- en
library_name: pytorch
base_model:
- microsoft/BiomedCLIP-PubMedBERT_256-vit_base_patch16_224
- torchvision/resnet101
datasets:
- ms-flair-mri
tags:
- medical-imaging
- brain-mri
- multiple-sclerosis
- binary-classification
- pytorch
---

    # Multiple Sclerosis Binary Classifier

    PyTorch checkpoint artifacts for the MultiAgentMedClassifier MS task.
    Contains a ResNet101 CNN checkpoint and a BiomedCLIP linear-probe checkpoint
    for classifying brain FLAIR MRI images as normal or multiple sclerosis.

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

    ## Model Description

    - Task: binary MS brain FLAIR MRI classification
    - CNN architecture: ResNet101
    - Vision-language backbone for probe: `microsoft/BiomedCLIP-PubMedBERT_256-vit_base_patch16_224`
    - Framework: PyTorch

    ## Classes

    - `normal`
    - `ms`

    The project-level BiomedCLIP labels are:

    - `normal brain FLAIR MRI`
    - `multiple sclerosis brain FLAIR MRI`

    ## Files

    - `ms/cnn/resnet101_MRI_ms_norm_final.pt`: ResNet101 CNN checkpoint for binary MS brain FLAIR MRI classification.
- `ms/biomedclip/linear_probe_BiomedCLIP_MRI_ms_norm_best.pt`: BiomedCLIP linear-probe checkpoint for binary MS brain FLAIR MRI classification.

    ## Training Details

    - Input size: 224 x 224 RGB
    - Normalization: ImageNet mean/std
    - CNN checkpoint: ResNet101 fine-tuned for the `ms` task
    - BiomedCLIP probe: linear/MLP probe over frozen BiomedCLIP image features (layer 6)

    ## Metrics

    | Model | Accuracy |
    |-------|----------|
    | ResNet101 CNN | 59.7% |

    Note: MS classification from FLAIR MRI is a challenging task; the relatively
    lower accuracy reflects the difficulty of distinguishing subtle white matter
    lesion patterns. Recompute metrics on your own held-out test set.

    ## 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-ms",
        filename="ms/cnn/resnet101_MRI_ms_norm_final.pt",
    )
    DEFAULT_CONFIG.model.cnn_checkpoints["ms"] = checkpoint_path
    classifier = CNNClassifier(DEFAULT_CONFIG.model, DEFAULT_CONFIG.preprocess)
    result = classifier.classify("path/to/brain_flair.png", task="ms")
    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.