Resource-efficient Medical Image Analysis with Self-adapting Forward-Forward Networks

arXiv:2406.14038v1 Announce Type: new
Abstract: We introduce a fast Self-adapting Forward-Forward Network (SaFF-Net) for medical imaging analysis, mitigating power consumption and resource limitations, which currently primarily stem from the prevalent reliance on back-propagation for model training and fine-tuning. Building upon the recently proposed Forward-Forward Algorithm (FFA), we introduce the Convolutional Forward-Forward Algorithm (CFFA), a parameter-efficient reformulation that is suitable for advanced image analysis and overcomes the speed and generalisation constraints of the original FFA. To address hyper-parameter sensitivity of FFAs …

abstract algorithm analysis arxiv building consumption convolutional cs.ai cs.cv fine-tuning forward-forward algorithm image imaging limitations medical medical imaging model training and fine-tuning network networks power power consumption propagation reliance stem training tuning type