用于FLIM网络的多层元胞自动机
Multi-level Cellular Automata for FLIM networks
AI总结：
针对深度学习显著目标检测中数据与架构的挑战，提出将FLIM网络与多层元胞自动机结合的方法，无需每张图用户交互，在医疗数据集上性能具竞争力，参数少且无需反向传播。
摘要：
在深度学习显著目标检测（deep SOD）及更广泛的深度学习领域中，对大量标注数据和复杂网络架构的需求构成了重大挑战。这一挑战在计算资源有限的发展中国家的医疗应用中尤为突出。结合现代与经典技术为在实现实际应用的同时保持竞争性能提供了一条路径。特征从图像标记中学习（FLIM）方法使专家能够通过用户绘制的标记设计卷积编码器，滤波器直接从这些标注中学习。最近的研究结果表明，将FLIM编码器与自适应解码器耦合可创建适用于SOD的轻量级网络，其参数数量远少于轻量级模型，且无需反向传播。元胞自动机（CA）方法已被证明在数据稀缺场景中有效，但需要适当的初始化——通常通过用户输入、先验或随机性。我们提出了这些方法的实际交集：使用FLIM网络利用专家知识初始化CA状态，无需对每张图像进行用户交互。通过解码FLIM网络各层的特征，我们可以同时初始化多个CA，创建多层框架。我们的方法利用不同网络层编码的分层知识，将多个显著性图合并为高质量的最终输出，其功能相当于CA集成。在两个具有挑战性的医疗数据集上的基准测试表明，我们的多层CA方法与deep SOD文献中的现有模型相比具有竞争力。
英文摘要：
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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