brain-mri-siglip

Brain MRI SigLIP

Brain MRI SigLIP is a 3D MRI vision-language representation model trained with a SigLIP-style image-text contrastive objective. This repository publishes the final saved stage2_joint_finetune checkpoint from the brain_mri_siglip_run_0509 experiment.

This checkpoint is intended as a research visual encoder for brain MRI downstream tasks and as a warm-start encoder for building a medical VLM. It is not a clinical diagnostic device.

Model Summary

• Base text tower: google/medsiglip-448
• Model class: BrainMRISiglipModel
• Vision input: single-channel 3D MRI volumes
• Expected volume shape: [1, 128, 192, 192]
• Projection dimension: 1152
• Patch size: [8, 16, 16]
• Training precision: bf16
• Training input format: preprocessed .pt tensors, float16, value range [-1, 1]

Training Context

This model was initialized from the brain_mri_siglip_run_0509/stage1_freeze_text checkpoint and then jointly fine-tuned with both vision and text towers trainable.

Training summary:

• Training samples: 950,720
• Validation samples: 67,450
• Validation samples with metadata_text: 32,278
• Stage 1: frozen text tower, vision-heavy training
• Stage 2: joint vision-text fine-tuning
• Stage 2 epochs configured: 8
• World size: 5
• Stage 2 per-device batch size: 160
• Stage 2 contrastive forward batch: 800
• Gradient checkpointing: text and vision enabled

Training-time retrieval evaluation used capped validation subsets and should be treated as monitoring rather than a final benchmark.

Loading

This model uses custom Transformers code. Load it with trust_remote_code=True.

import torch
from transformers import AutoModel, AutoProcessor

repo_id = "shenxiaochen/brain-mri-siglip"
device = "cuda" if torch.cuda.is_available() else "cpu"

model = AutoModel.from_pretrained(
    repo_id,
    trust_remote_code=True,
    dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32,
).to(device).eval()

processor = AutoProcessor.from_pretrained(
    repo_id,
    trust_remote_code=True,
)

NIfTI Preprocessing

For reproducible inference from NIfTI files, pass paths directly to the saved processor. This repository includes the offline-aligned preprocessing implementation used to match the training tensor distribution.

nifti_path = "/path/to/brain_mri.nii.gz"

inputs = processor(
    volumes=nifti_path,
    return_tensors="pt",
)
pixel_values = inputs["pixel_values"].to(device)

if torch.cuda.is_available():
    pixel_values = pixel_values.to(dtype=torch.bfloat16)

with torch.inference_mode():
    image_embeds = model.get_image_features(pixel_values=pixel_values)

print(pixel_values.shape)  # [1, 1, 128, 192, 192]
print(image_embeds.shape)  # [1, 1152]

The saved path-based preprocessing recipe is:

• canonicalize image orientation to closest RAS
• build foreground mask with threshold 1e-3
• keep the largest connected foreground component
• crop foreground with 5mm margin
• normalize foreground intensities with 0.5/99.5 percentiles
• map intensities to [-1, 1]
• resample to spacing (1.25, 1.0, 1.0)
• downscale to fit [128, 192, 192]
• center-pad with background value -1.0

The exact settings are saved in preprocessor_config.json and processor_config.json.

Using Preprocessed .pt Inputs

If your data is already stored as the same offline preprocessed tensors used during training, you can load it directly:

payload = torch.load("/path/to/sample.pt", map_location="cpu")
pixel_values = payload["pixel_values"] if isinstance(payload, dict) else payload

if pixel_values.ndim == 4:
    pixel_values = pixel_values.unsqueeze(0)

pixel_values = pixel_values.to(device=device, dtype=torch.bfloat16)

with torch.inference_mode():
    image_embeds = model.get_image_features(pixel_values=pixel_values)

Expected tensor format:

• shape [1, 128, 192, 192] for one volume, or [B, 1, 128, 192, 192] for a batch
• values in [-1, 1]
• padded background voxels near -1.0

VLM Integration Notes

For VLM construction, use the 3D vision tower as a visual backbone and add a projector, Q-Former, Perceiver resampler, or other token compressor before connecting to an LLM.

A practical downstream recipe is:

1. Freeze this MRI encoder and train only the multimodal projector/resampler.
2. Evaluate downstream classification, retrieval, report alignment, or instruction-following behavior.
3. Optionally unfreeze the top vision layers with a much smaller learning rate.

Limitations

• This checkpoint was trained for representation learning, not diagnosis.
• Performance should be validated on task-specific subject-level or study-level splits.
• Scanner, protocol, site, and preprocessing differences can affect embeddings.
• External users should preserve the saved preprocessing pipeline for NIfTI inference.
• Retrieval monitoring during training is not a substitute for downstream clinical validation.

Citation

If you use this checkpoint, please cite this model repository and the upstream MedSigLIP model where appropriate.