BrainIAC Model

This is the official implementation of the BrainIAC model, a 3D ResNet50-based architecture designed for brain image analysis.

Model Description

BrainIAC is built on a modified ResNet50 architecture that processes 3D brain imaging data. The model has been adapted to handle volumetric inputs through 3D convolutions and produces feature vectors that capture relevant brain imaging characteristics.

Model Architecture

Base Architecture: ResNet50 (modified for 3D)
Input: 3D brain volumes [batch_size, 1, D, H, W]
Output: Feature vector of dimension 2048
First layer: 3D convolution (1 channel input)
Final layer: Identity (returns features directly)

Usage

from transformers import AutoModel
import torch

# Load model
model = AutoModel.from_pretrained("your-username/brainiac")
model.eval()

# Prepare your input tensor
# Adjust D, H, W according to your requirements
batch_size = 1
D, H, W = 16, 224, 224  # Example dimensions
input_tensor = torch.randn(batch_size, 1, D, H, W)

# Get features
with torch.no_grad():
    features = model(input_tensor)

print(f"Output feature shape: {features.shape}")  # Should be [batch_size, 2048]

Requirements

torch>=1.9.0
monai
transformers

Citation

If you use this model in your research, please cite: [Add your citation information]

License

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