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

TL;DR

This paper presents SaFF-Net, a resource-efficient medical image analysis model using a self-adapting Forward-Forward Algorithm that reduces power consumption and overcomes limitations of back-propagation.

Contribution

It introduces CFFA and SaFF-Net, enabling efficient, hyper-parameter adaptive training for Forward-Forward Networks in medical imaging tasks.

Findings

FFA-based networks with fewer parameters compete with standard models.

SaFF-Net performs well in one-shot learning and large batch scenarios.

The approach reduces power consumption and training complexity.

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 we are also introducing a self-adapting framework SaFF-Net fine-tuning parameters during warmup and training in parallel. Our approach enables more effective model training and eliminates the previously essential requirement for an arbitrarily chosen Goodness function in FFA. We evaluate our approach on several benchmarking datasets in comparison with standard Back-Propagation (BP) neural networks showing that FFA-based networks with notably fewer parameters and function evaluations can compete with standard models, especially, in one-shot scenarios and large batch sizes. The code will be available at the time of the conference.