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README.md

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+---
+{}
+---
+---
+    license: other
+    library_name: pytorch
+    tags:
+    - medical-imaging
+    - brain-mri
+    - tumor-classification
+    - binary-classification
+    - pytorch
+    ---
+
+    # Brain Tumor Binary Classifier
+
+    PyTorch checkpoint artifacts for the MultiAgentMedClassifier binary brain tumor
+    MRI task. The repository contains a VGG16 CNN classifier checkpoint and,
+    optionally, a BiomedCLIP linear-probe checkpoint for classifying brain MRI
+    images as normal or tumor.
+
+    These are checkpoint files for the accompanying project loaders, not standalone
+    Transformers models.
+
+    ## Model Description
+
+    - Task: binary brain tumor MRI classification
+    - CNN architecture: VGG16
+    - Vision-language backbone for probe: `microsoft/BiomedCLIP-PubMedBERT_256-vit_base_patch16_224`
+    - Framework: PyTorch
+
+    ## Classes
+
+    - `normal`
+    - `tumor`
+
+    The project-level BiomedCLIP labels are:
+
+    - `normal brain MRI`
+    - `brain tumor MRI`
+
+    ## Files
+
+    - `binary_tumor/cnn/vgg16_MRI_tumor_binary_norm_final.pt`: VGG16 CNN checkpoint for binary brain tumor MRI classification.
+- `binary_tumor/biomedclip/linear_probe_BiomedCLIP_MRI_tumor_binary_norm_best.pt`: BiomedCLIP linear-probe checkpoint for binary brain tumor MRI classification.
+
+    ## Dataset
+
+    Trained/evaluated for the binary tumor task using brain MRI tumor/normal data.
+    The local evaluation script supports the Br35H binary layout:
+
+    - `data/Br35H/yes`: brain tumor MRI
+    - `data/Br35H/no`: normal brain MRI
+
+    Update this section if you publish a model trained on a different dataset split
+    or source.
+
+    ## Training Details
+
+    - Input size: 224 x 224 RGB
+    - Normalization: ImageNet mean/std
+    - CNN checkpoint: VGG16 fine-tuned for the `binary_tumor` task
+    - BiomedCLIP probe: linear/MLP probe over frozen BiomedCLIP image features
+
+    ## Metrics
+
+    Evaluation is intended for the `binary_tumor` task on brain MRI tumor/normal
+    datasets such as the Br35H binary layout described above. Recompute metrics on
+    your held-out test set before using this model in a new domain or workflow.
+
+    ## Inference Example
+
+    Download the checkpoint from Hugging Face and point the local project config at
+    it:
+
+    ```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-binary-tumor",
+        filename="binary_tumor/cnn/vgg16_MRI_tumor_binary_norm_final.pt",
+    )
+
+    DEFAULT_CONFIG.model.cnn_checkpoints["binary_tumor"] = checkpoint_path
+
+    classifier = CNNClassifier(DEFAULT_CONFIG.model, DEFAULT_CONFIG.preprocess)
+    result = classifier.classify("path/to/brain_mri.png", task="binary_tumor")
+    print(result)
+    ```
+
+    For the BiomedCLIP probe:
+
+    ```python
+    from huggingface_hub import hf_hub_download
+
+    from agents.biomedclip_tool import BiomedCLIPTool
+    from config import DEFAULT_CONFIG
+
+    probe_path = hf_hub_download(
+        repo_id="tamara-kostova/multiagentmed-binary-tumor",
+        filename=(
+            "binary_tumor/biomedclip/"
+            "linear_probe_BiomedCLIP_MRI_tumor_binary_norm_best.pt"
+        ),
+    )
+
+    DEFAULT_CONFIG.model.biomedclip_probe_checkpoints["binary_tumor"] = probe_path
+
+    tool = BiomedCLIPTool(DEFAULT_CONFIG.model, DEFAULT_CONFIG.preprocess)
+    result = tool.classify("path/to/brain_mri.png", task="binary_tumor")
+    print(result)
+    ```
+
+    ## Intended Use
+
+    This model is intended for research and experimentation in automated
+    neuroimaging pipelines. It may be useful for prototype triage, benchmarking,
+    and comparison against other image classifiers.
+
+    It is not a medical device and should not be used as the sole basis for
+    diagnosis, treatment decisions, or patient management.
+
+    ## Loading In This Repository
+
+    Use these files with this repository's local loaders:
+
+    - CNN: `config.ModelConfig.cnn_checkpoints["binary_tumor"]`
+    - BiomedCLIP probe: `config.ModelConfig.biomedclip_probe_checkpoints["binary_tumor"]`
+