Forward-Forward Learning Without Back-Propagation Achieves Competitive Anomaly Detection in Medical Imaging

A self-adaptive forward-forward network (SaFF-AD) achieved competitive or superior anomaly and out-of-distribution detection versus back-propagation-trained models while using substantially fewer parameters. The method requires no auxiliary networks and generates an intrinsic go…

• Design: Comparative experiments across multiple medical imaging benchmarks using a reformulated convolutional forward-forward algorithm (CFFA) tailored to high-dimensional images.
• Key secondary finding: The goodness signal enables stable, self-supervised anomaly detection in one-shot training regimes; exact AUC or sensitivity/specificity figures were not reported in the source abstract.
• Limitation: Performance reported on benchmarks; prospective clinical validation and comparison with current state-of-the-art back-propagation models on real-world radiology workflows are still needed.

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