bibtext: @InProceedings{ Kim_Parameter_MICCAI2024,
                   author = { Kim, Yumin and Choi, Gayoon and Hwang, Seong Jae },
                   title = { { Parameter Efficient Fine Tuning for Multi-scanner PET to PET Reconstruction } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15007 },
                   month = {October},
                   pages = { pending },
                }
abstract: Reducing scan time in Positron Emission Tomography (PET) imaging while maintaining high-quality images is crucial for minimizing patient discomfort and radiation exposure. Due to the limited size of datasets and distribution discrepancy across scanners in medical imaging, fine-tuning in a parameter-efficient and effective manner is on the rise. Motivated by the potential of Parameter Efficient Fine-Tuning (PEFT), we aim to address these issues by effectively leveraging PEFT to improve limited data and GPU resource issues in multi-scanner setups. In this paper, we introduce PETITE, Parameter Efficient Fine-Tuning for MultI-scanner PET to PET REconstruction, which represents the optimal PEFT combination when independently applying encoder-decoder components to each model architecture. To the best of our knowledge, this study is the first to systematically explore the efficacy of diverse PEFT techniques in medical imaging reconstruction tasks via prevalent encoder-decoder models. This investigation, in particular, brings intriguing insights into PETITE as we show further improvements by treating the encoder and decoder separately and mixing different PEFT methods, namely, Mix-PEFT. Using multi-scanner PET datasets comprised of five different scanners, we extensively test the cross-scanner PET scan time reduction performances (i.e., a model pre-trained on one scanner is fine-tuned on a different scanner) of 21 feasible Mix-PEFT combinations to derive optimal PETITE. We show that training with less than 1% parameters using PETITE performs on par with full fine-tuning (i.e., 100% parameter). Code is available at: https://github.com/MICV-yonsei/PETITE
title: Parameter Efficient Fine Tuning for Multi-scanner PET to PET Reconstruction
authors: Kim, Yumin Choi, Gayoon Hwang, Seong Jae