Cascading size-dependent deep propagation (CADP): Addressing over-smoothing in graph few-shot dermatology classification

Graphs play a critical role in capturing complex data relationships, particularly in few-shot learning tasks. However, one of the major challenges in graph-based models, such as Graph Neural Networks (GNNs), is the issue of over-smoothing, which diminishes the discriminative power of node representations. This problem arises when GNNs aggregate information from too large a neighborhood, leading to homogenization of node features. To overcome this limitation, we propose Cascading Size-Dependent Deep Propagation (CADP), a novel approach designed to mitigate over-smoothing in graph-based few-shot learning, with a particular focus on improving skin disease classification. The model constructs the graph by employing a convolutional neural network (CNN) to extract feature representations from a small set of support and query images, where the nodes represent the extracted features, and the edges reflect the similarity between them. To improve feature representation and prevent over-smoothing, the model decouples the feature propagation process from the neural network to avoid repeated nonlinear transformations that lead to over-smoothing, enabling deeper information flow while preserving discriminative features. Then the initial support labels are integrated with the early prediction labels of query images, which are generated by a Multi-Layer Perceptron (MLP). Furthermore, this aggregated data is optimized through deep label propagation, which leverages the underlying graph structure to enhance classification accuracy. The propagation depths are controlled by the hyperparameters $K_1$ and $K_2$, which are determined based on graph size, to regulate how extensively features and labels are propagated. We evaluate our approach on three dermatology datasets: ISIC 2018, Derm7pt, and SD-198, achieving 78.3 %, 79.29 %, and 91.92 % accuracy, respectively, in the 2-way 5-shot setting. CADP outperforms existing methods on all datasets, demonstrating its effectiveness in skin disease classification.