Lesion segmentation is an essential task in medical imaging to support diagnosis and assessment of pathologies. While deep learning models have shown success in various domains, their reliance on large-scale annotated datasets limits applicability in the medical domain due to labeling cost. To address this issue, recent studies in medical image segmentation have utilized clinical texts as complementary semantic cues without additional annotations. However, most existing methods utilize a single textual embedding and fail to capture hierarchical interactions between language and visual features, which limits their ability to leverage fine-grained cues essential for precise and detailed segmentation. In this regime, we propose Hierarchical Visual-Textual Mixing Network (HiMix), a novel multi-modal segmentation framework that mixes multi-scale image and text representations throughout the mask decoding process. HiMix progressively injects hierarchical text embedding, from high-level semantics to fine-grained spatial details, into corresponding image decoder layers to bridge the modality gap and enhance visual feature refinement at multiple levels of abstraction. Experiments on the QaTa-COV19, MosMed-Data+ and Kvasir-SEG datasets demonstrate that HiMix consistently outperforms uni-modal and multi-modal methods. Furthermore, HiMix exhibits strong generalization to unstructured textual formats, highlighting its practical applicability in real-world clinical scenarios.

Various Graph Neural Networks (GNN) have been successful in analyzing data in non-Euclidean spaces, however, they have limitations such as oversmoothing, i.e., information becomes excessively averaged as the number of hidden layers increases. The issue stems from the intrinsic formulation of conventional graph convolution where the nodal features are aggregated from a direct neighborhood per layer across the entire nodes in the graph. As setting different number of hidden layers per node is infeasible, recent works leverage a diffusion kernel to redefine the graph structure and incorporate information from farther nodes. Unfortunately, such approaches suffer from heavy diagonalization of a graph Laplacian or learning a large transform matrix. In this regards, we propose a diffusion learning framework where the range of feature aggregation is controlled by the scale of a diffusion kernel. For efficient computation, we derive closed-form derivatives of approximations of the graph convolution with respect to the scale, so that node-wise range can be adaptively learned.With a downstream classifier, the entire framework is made trainable in an end-to-end manner. Our model is tested on various standard datasets for node-wise classification for the state-of-the-art performance, and it is also validated on a real-world brain network data for graph classifications to demonstrate its practicality for Alzheimer classification.

Analysis of neurodegenerative diseases on brain connectomes is important in facilitating early diagnosis and predicting its onset. However, investigation of the progressive and irreversible dynamics of these diseases remains underexplored in cross-sectional studies as its diagnostic groups are considered independent. Also, as in many real-world graphs, brain networks exhibit intricate structures with both homophily and heterophily. To address these challenges, we propose Adaptive Graph diffusion network with Temporal regularization (AGT). AGT introduces node-wise convolution to adaptively capture low (i.e., homophily) and high-frequency (i.e., heterophily) characteristics within an optimally tailored range for each node. Moreover, AGT captures sequential variations within progressive diagnostic groups with a novel temporal regularization, considering the relative feature distance between the groups in the latent space. As a result, our proposed model yields interpretable results at both node-level and group-level. The superiority of our method is validated on two neurodegenerative disease benchmarks for graph classification: Alzheimer’s Disease Neuroimaging Initiative (ADNI) and Parkinson’s Progression Markers Initiative (PPMI) datasets.