CnvLoRA and AdaBN Based Domain Adaptation via Self-Training
The code repository for paper "ConvLoRA and AdaBN based Domain Adaptation via Self-Training", accepted at IEEE ISBI 2024 in PyTorch.
We propose Convolutional Low-Rank Adaptation (ConvLoRA), as an adaptation of Low-Rank Domain Adaptation (LoRA) in LLMs. ConvLoRA is specifically designed for application in Convolutional Neural Networks (CNNs), presenting a novel approach to address domain adaptation challenges in the context of image data. Instead of creating dedicated fine-tuned models for multiple target domains, each with the same number of parameters as the base model, we inject several ConvLoRA adapters into the base model pre-trained on the source domain, and only adapt the ConvLoRA parameters, while keeping all other parameters. This method allows faster updates by adapting only a small set of domain specific parameters.
Calgary-Campinas (CC359) dataset is a multi-vendor (GE, Philips, Siemens), multi-field strength (1 5, 3) magnetic resonance (MR) T1-weighted volumetric brain imaging dataset. It has six different domains and contains 359 3D brain MR image volumes, primarily focused on the task of skull stripping.
Install deep_pipe:
git clone https://github.com/neuro-ml/deep_pipe.git
cd deep_pipe
git checkout 5c08d5759d51c0731cc636c2866bb3a538ffab7a
pip install -e .
Original repository: https://github.com/deepmind/surface-distance
The following arguments are required to run the code. The details are in <main.py>
Task Related Arguments
dataset: Option for the dataset, default to CC359
site: Site in CC359 dataset
step: Specifies stage of adaptation pipeline (base_model, refine, adapt)
seed: Seed value for reproducibility
test: Flag to activate inference
Training base model
python main.py --config ./config/baseline.json --data "cc359" --site 2 --step "base_model" --seed 1234 --wandb_mode "online" --suffix <"user defined">
Training ESH model
python main.py --config ./config/feature_seg.json --data "cc359" --site 2 --step "feature_segmentor" --seed 1234 --wandb_mode "online" --suffix <"user defined">
Adaptation
python main.py --config ./config/refinment.json --data "cc359" --site 3 --step "adapt" --seed 1234 --wandb_mode "online" --suffix <"user defined">
python main.py --config ./config/test_baseline.json --data "cc359" --site 3 --step "test" --seed 1234 ---wandb_mode "online" --suffix <"user defined"> --test test --adapt "lora"
Feel free to raise an issue or contact me at [email protected] for queries and discussions.