MedMerge: Merging Models for Effective Transfer Learning to Medical Imaging Tasks
<|reference_start|>MedMerge: Merging Models for Effective Transfer Learning to Medical Imaging Tasks: Transfer learning has become a powerful tool to initialize deep learning models to achieve faster convergence and higher performance. This is especially useful in the medical imaging analysis domain, where data scarcity limits possible performance gains for deep learning models. Some advancements have been made in boosting the transfer learning performance gain by merging models starting from the same initialization. However, in the medical imaging analysis domain, there is an opportunity in merging models starting from different initialisations, thus combining the features learnt from different tasks. In this work, we propose MedMerge, a method whereby the weights of different models can be merged, and their features can be effectively utilized to boost performance on a new task. With MedMerge, we learn kernel-level weights that can later be used to merge the models into a single model, even when starting from different initializations. Testing on various medical imaging analysis tasks, we show that our merged model can achieve significant performance gains, with up to 3% improvement on the F1 score. The code implementation of this work will be available at www.github.com/BioMedIA-MBZUAI/MedMerge.<|reference_end|>
@article{almakky2024medmerge:,
    title={MedMerge: Merging Models for Effective Transfer Learning to Medical
  Imaging Tasks},
    author={Ibrahim Almakky, Santosh Sanjeev, Anees Ur Rehman Hashmi, Mohammad
  Areeb Qazi, Mohammad Yaqub},
    journal={arXiv preprint arXiv:2403.11646},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.11646},
    primaryClass={cs.CV}
}