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text2brain

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Generating brain activation maps from free-form text query

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# Text2Brain
![brain interpreter review](https://raw.githubusercontent.com/ngohgia/text2brain/main/readme_figures/brain_interpreter.gif)

Generating brain activation maps from free-form text query

## Reference
Gia H. Ngo, Minh Nguyen, Nancy F. Chen, Mert R. Sabuncu. [**Text2Brain: Synthesis of Brain Activation Maps from Free-form Text Query**](https://link.springer.com/chapter/10.1007/978-3-030-87234-2_57). In International Conference on Medical Image Computing and Computer-Assisted Intervention, 2021. [arxiv](https://arxiv.org/abs/2109.13814)

----

## Overview

Text2Brain is a search engine for efficiently combing through rapidly growing wealth of neuroimaging literature brain activation patterns.
It accepts not only keywords but also flexible free-form text queries.
It encodes the text queries using a finetuned Transformer encoder (SciBERT) and generates whole-brain activation maps using a 3D convolutional neural network (CNN).

Demo is available at: [braininterpreter.com](braininterpreter.com)

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## Outputs for Synonymous Queries

Activation maps predicted by Text2Brain for 3 synonymous queries: `default network`, `self-generated thought`, and `task-unrelated thought`.
The ground-truth activation map is also included in the figure.

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## Setting Up Text2Brain Project

0. Install [Anaconda](https://docs.anaconda.com/anaconda/install/index.html)
1. Clone this project from Github to some place on your computer (e.g. `/home/gia/text2brain`)
2. Create a Conda environment using the `env.yml` file
    ```bash
        conda env create -f env.yml -n text2brain
    ```
3. Download a checkpoint of the Text2Brain model from [Google Drive](https://drive.google.com/file/d/1mBO1frmlSMgXGN-F1WKQKlCO0gIk_MHq/view?usp=share_link)
4. Extract the downloaded file. You should see a file named `best_loss.pth`.
    ```bash
        tar -xzvf text2brain_checkpoint.tar.gz
    ```
5. Move the file `best_loss.pth` into the project directory (e.g. `/home/gia/text2brain`)
6. Download the pre-trained uncased SciBERT model using [this link](https://s3-us-west-2.amazonaws.com/ai2-s2-research/scibert/huggingface_pytorch/scibert_scivocab_uncased.tar)
7. Extract the downloaded file.
    ```bash
        tar -xvf scibert_scivocab_uncased.tar
    ```
8. Move the `scibert_scivocab_uncased` folder into the project directory (e.g. `/home/gia/text2brain`)
9. Activate the Conda environment
    ```bash
        conda activate text2brain
    ```
10. To generate brain activation maps from free-form text query, run `python predict.py <input_query> <output_file>`. For example,
    ```bash
    python predict.py "self-generated thought" prediction.nii.gz
    ```

----

## Bugs and Questions

Please contact Gia at ngohoanggia@gmail.com

text2brain

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