Resource-Efficient Medical Image Analysis with Self-adapting Forward-Forward Networks
Müller J, Kainz B (2025)
Publication Type: Conference contribution
Publication year: 2025
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 15242 LNCS
Pages Range: 180-190
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Marrakesh
ISBN: 9783031732928
DOI: 10.1007/978-3-031-73290-4_18
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.
Authors with CRIS profile
Johanna Müller
Professur für Image Data Exploration and Analysis
Bernhard Kainz
Professur für Image Data Exploration and Analysis
How to cite
APA:
Müller, J., & Kainz, B. (2025). Resource-Efficient Medical Image Analysis with Self-adapting Forward-Forward Networks. In Xuanang Xu, Zhiming Cui, Kaicong Sun, Islem Rekik, Xi Ouyang (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 180-190). Marrakesh, MA: Springer Science and Business Media Deutschland GmbH.
MLA:
Müller, Johanna, and Bernhard Kainz. "Resource-Efficient Medical Image Analysis with Self-adapting Forward-Forward Networks." Proceedings of the 15th International Workshop on Machine Learning in Medical Imaging, MLMI 2024 was held in conjunction with the 27th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024, Marrakesh Ed. Xuanang Xu, Zhiming Cui, Kaicong Sun, Islem Rekik, Xi Ouyang, Springer Science and Business Media Deutschland GmbH, 2025. 180-190.
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