bibtext: @InProceedings{ San_FissionFusion_MICCAI2024,
                   author = { Sanjeev, Santosh and Zhaksylyk, Nuren and Almakky, Ibrahim and Hashmi, Anees Ur Rehman and Qazi, Mohammad Areeb and Yaqub, Mohammad },
                   title = { { FissionFusion: Fast Geometric Generation and Hierarchical Souping for Medical Image Analysis } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15012 },
                   month = {October},
                   pages = { pending },
                }
abstract: The scarcity of well-annotated medical datasets requires leveraging transfer learning from broader datasets like ImageNet or pre-trained models like CLIP. Model soups averages multiple fine-tuned models aiming to improve performance on In-Domain (ID) tasks and enhance robustness on Out-of-Distribution (OOD) datasets. However, applying these methods to the medical imaging domain faces challenges and results in suboptimal performance. This is primarily due to differences in error surface characteristics that stem from data complexities such as heterogeneity, domain shift, class imbalance, and distributional shifts between training and testing phases. To address this issue, we propose a hierarchical merging approach that involves local and global aggregation of models at various levels based on models’ hyperparameter configurations. Furthermore, to alleviate the need for training a large number of models in the hyperparameter search, we introduce a computationally efficient method using a cyclical learning rate scheduler to produce multiple models for aggregation in the weight space. Our method demonstrates significant improvements over the model souping approach across multiple datasets (around 6% gain in HAM10000 and CheXpert datasets) while maintaining low computational costs for model generation and selection. Moreover, we achieve better results on OOD datasets compared to model soups. Code is available at https://github.com/BioMedIA-MBZUAI/FissionFusion.
title: FissionFusion: Fast Geometric Generation and Hierarchical Souping for Medical Image Analysis
authors: Sanjeev, Santosh Zhaksylyk, Nuren Almakky, Ibrahim Hashmi, Anees Ur Rehman Qazi, Mohammad Areeb Yaqub, Mohammad