Multi-level Cellular Automata for FLIM networks

• URL: http://arxiv.org/abs/2504.11406v1
• Date: Tue, 15 Apr 2025 17:22:24 GMT
• Title: Multi-level Cellular Automata for FLIM networks
• Authors: Felipe Crispim Salvagnini, Jancarlo F. Gomes, Cid A. N. Santos, Silvio Jamil F. Guimarães, Alexandre X. Falcão,
• Abstract summary: We propose a new approach to deep-learning Salient Object Detection.<n>It combines modern and classical techniques to maintain competitive performance.<n>We show that our method is competitive with established models in the deep SOD literature.
• Score: 40.83004529604423
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The necessity of abundant annotated data and complex network architectures presents a significant challenge in deep-learning Salient Object Detection (deep SOD) and across the broader deep-learning landscape. This challenge is particularly acute in medical applications in developing countries with limited computational resources. Combining modern and classical techniques offers a path to maintaining competitive performance while enabling practical applications. Feature Learning from Image Markers (FLIM) methodology empowers experts to design convolutional encoders through user-drawn markers, with filters learned directly from these annotations. Recent findings demonstrate that coupling a FLIM encoder with an adaptive decoder creates a flyweight network suitable for SOD, requiring significantly fewer parameters than lightweight models and eliminating the need for backpropagation. Cellular Automata (CA) methods have proven successful in data-scarce scenarios but require proper initialization -- typically through user input, priors, or randomness. We propose a practical intersection of these approaches: using FLIM networks to initialize CA states with expert knowledge without requiring user interaction for each image. By decoding features from each level of a FLIM network, we can initialize multiple CAs simultaneously, creating a multi-level framework. Our method leverages the hierarchical knowledge encoded across different network layers, merging multiple saliency maps into a high-quality final output that functions as a CA ensemble. Benchmarks across two challenging medical datasets demonstrate the competitiveness of our multi-level CA approach compared to established models in the deep SOD literature.

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