Missing critical modalities in medical imaging poses significant challenges for AI-driven diagnostic systems, particularly in scenarios where limited modalities must suffice for downstream tasks. Existing approaches often fail to fully leverage privileged features available only at training or address the information gap between privileged and limited modalities, resulting in suboptimal performance. To address this, we propose a unified, dual-stage Disentanglement-AligNmenT framEwork (DANTE), which uses InformationTheoretic Regularization and Cross-Modal Reconstruction to decompose full-modality information into alignable and privileged-exclusive components. In the first stage, a self-supervised pre-training strategy based on cross-modal reconstruction acts as a proxy task to implicitly incentivize disentangled representations. In the second stage, we present an information-theoretic regularization to explicitly maximize the transfer of privileged knowledge through two novel modules: (1) a Mutual Alignment Module that employs multilevel bidirectional alignment between limited-modality features and alignable features, enhancing cross-modal representation consistency; (2) a Privileged Compaction Module that restricts the privileged-exclusive information flow, promoting the integration of task-relevant content into alignable representations. Experimental results on three challenging medical datasets demonstrate that DANTE achieves state-of-the-art performance, demonstrating its effectiveness in leveraging privileged guidance under modality scarcity, and exhibits broad applicability across diverse medical imaging scenarios.

Interpretable image classification is crucial for making decisions in high-stakes scenarios. Recent advancements have demonstrated that interpretable models can achieve performance comparable to black-box models by integrating Visual Language Models (VLMs) with Concept Bottleneck Models (CBMs). These models explain their predictions by calculating the weighted sum of similarities between the image representation and predefined text embeddings. However, selecting textual descriptors is subjective, and relying solely on textual information may not capture the complexities of visual data, impacting both interpretability and performance. To address these limitations, this work explores the cross-modality interpretation of class-related concepts in image classification. Specifically, we propose decomposed concept bottleneck model (DCBM), which utilizes a set of decomposed visual concepts that are extracted directly from images instead of predefined text concepts. The decomposition of concepts is achieved through vector projection onto concept decomposition vectors (CDVs), which can be interpreted across both textual and visual modalities. We introduce a quintuple notion of concepts and a concept-sample distribution theorem, which enables the localization of decomposed concepts in images using the Segment Anything Model (SAM) with automatically generated prompts. Experimental results demonstrate that DCBM achieves competitive performance compared to non-interpretable models, with a 3.42% improvement in classification accuracy and a 66.27% improvement in image-text groundability compared to other VLM-based CBMs. Furthermore, we evaluate the benefits of employing automatically generated prompts in SAM for interpreting visual concepts, in contrast to prompts created by human operators.

Unraveling the intricate network of associations among microRNAs (miRNAs), genes, and diseases is pivotal for deciphering molecular mechanisms, refining disease diagnosis, and crafting targeted therapies. Computational strategies, leveraging link prediction within biological graphs, present a cost-efficient alternative to high-cost empirical assays. However, while plenty of methods excel at predicting specific associations, such as miRNA-disease associations (MDAs), miRNA-target interactions (MTIs), and disease-gene associations (DGAs), a holistic approach harnessing diverse data sources for multifaceted association prediction remains largely unexplored. The limited availability of high-quality data, as vitro experiments to comprehensively confirm associations are often expensive and time-consuming, results in a sparse and noisy heterogeneous graph, hindering an accurate prediction of these complex associations. To address this challenge, we propose a novel framework called Global-local aware Heterogeneous Graph Contrastive Learning (GlaHGCL). GlaHGCL combines global and local contrastive learning to improve node embeddings in the heterogeneous graph. In particular, global contrastive learning enhances the robustness of node embeddings against noise by aligning global representations of the original graph and its augmented counterpart. Local contrastive learning enforces representation consistency between functionally similar or connected nodes across diverse data sources, effectively leveraging data heterogeneity and mitigating the issue of data scarcity. The refined node representations are applied to downstream tasks, such as MDA, MTI, and DGA prediction. Experiments show GlaHGCL outperforming state-of-the-art methods, and case studies further demonstrate its ability to accurately uncover new associations among miRNAs, genes, and diseases. We have made the datasets and source code publicly available at https://github.com/Sue-syx/GlaHGCL.