MTMed3D

In this work, we propose MTMed3D, a novel Multi-task Transformer-based model to address the limitations of single-task models by jointly performing brain tumor detection, segmentation, and glioma grading classification. Our model utilizes a Swin Transformer backbone and employs hard parameter sharing, which involves sharing encoders across all tasks, followed by task-specific decoders for each task. The proposed framework is evaluated on the BraTS 2018 and BraTS 2019 datasets, achieving promising results across all three tasks. Additionally, we compare the performance and efficiency of our multi-task model with that of single-task models. Our multi-task model demonstrates a significant reduction in computational costs and faster inference speed while maintaining comparable performance to the single-task models. The architecture of MTMed3D is illustrated below:

Installation

Create a Conda Environment conda create -n env_name python=3.8 -y conda install pytorch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 pytorch-cuda=11.8 -c pytorch -c nvidia Install Additional Requirements git clone https://github.com/fanlimua/MTMed3D.git cd MTMed3D pip3 install -r requirements.txt

Dataset

MTMed3D is tested on the BraTS 2018 dataset for brain tumor detection, segmentation, and glioma grading classification. Download the dataset from the official BraTS 2018 website.

Training

To train MTMed3D, run the following command: python main.py --json_file /path_to_data_annotation.json \ --data_dir /path_to_imageTr2018 \ --seg_label_dir /path_to_labelTr2018 \ --num_epochs 200 \ --task multi \ --multi_opt GradNorm

Testing

Before running inference, download the pre-trained models from Google Drive. Run the testing script: python multi_test.py --json_file /path_to_data_annotation.json \ --data_dir /path_to_imageTr2018 \ --seg_label_dir /path_to_labelTr2018 \ --mode testing

Output Visualization

The figure below illustrates the output of our MTMed3D model compared to the ground truth. The left three columns show slices from three different directions of an HGG (High-Grade Glioma) sample. The right three columns show slices from three different directions of an LGG (Low-Grade Glioma) sample. The top row represents the ground truth labels. The bottom row represents the predictions generated by our MTMed3D model.

Citation

@misc{Li_MTMed3D_2025, author = {Li, Fan}, month = feb, title = {{MTMed3D: A Multi-Task Transformer-Based Model for 3D Medical Imaging}}, url = {https://github.com/fanlimua/MTMed3D.git}, year = {2025} }