Face Super-Resolution (FSR), aiming to improve the quality of Low-Resolution (LR) facial images, has been greatly propelled by the deep learning techniques. However, existing approaches, whether based on Convolutional Neural Networks (CNNs) or Transformers, are either inherently damaging facial structures limited by their architectures or failing to capture essential multi-scale textures due to the rigid receptive fields. To address these concerns, we propose a novel dual-domain feature interaction method called Spatial-frequency Multi-scale feature Learning Network (SMLNet) for FSR by employing a dual-branch architecture. Specifically, the frequency branch captures high-quality global structures and fine high-frequency details, while the spatial branch operates complementarily to preserve fine-grained local texture patterns. Moreover, we further introduce a Multi-scale Spatial-frequency feature Interaction Module (MSIM), which combines a Multi-scale Feature Extraction Block (MFEB) and a Spatial-Frequency feature Interaction Module (SFIM) to interact and aggregate multi-level complementary features from the dual branches. Extensive quantitative experiments and qualitative analyses across multiple datasets, together with evaluations on real-world images, demonstrate that the proposed SMLNet significantly outperforms other state-of-the-art methods.

Immunohistochemical (IHC) biomarker prediction greatly benefits from multimodal data fusion. However, the simultaneous acquisition of genomic and pathological data is often constrained by cost or technical limitations. To address this, we propose a novel Genomics-guided Multimodal Knowledge Decomposition Network (GMKDN), a framework that effectively integrates genomics and pathology data during training while dynamically adapting to available data during inference. GMKDN introduces two key innovations: 1) the Batch-Sample Multimodal Knowledge Decomposition (BMKD) module, which decomposes input features into pathology-specific, modality-general, and genomics-specific components to reduce redundancy and enhance knowledge transferability, and 2) the Online Similarity-Preserving Knowledge Distillation (OSKD) module, which optimizes activation similarity matrices to facilitate robust knowledge transfer between teacher and student models. The BMKD module improves generalization across modalities, while the OSKD module enhances model robustness, particularly when certain modalities are unavailable during inference. Extensive evaluations conducted on the TCGA-BRCA dataset and an external test cohort (QHSU) demonstrate that GMKDN consistently outperforms state-of-the-art (SOTA) slide-based multiple instance learning (MIL) approaches as well as existing multimodal learning models, establishing a new benchmark for breast cancer biomarker prediction. Our code is available at https://github.com/qiyuanzz/GMKDN.

Spatial transcriptomics measures gene expression and spatial location in tissue sections, but high sequencing costs limit adoption. Predicting spatial gene expression from histopathology offers a lower-cost alternative, yet current models rely on local/adjacent regions, lack effective multi-scale integration, and miss broader histological context. Performance is further impacted by variability across platforms and the heterogeneity of cancer tissues. In this study, we propose MSFF-ST, a Multi-Scale Feature Fusion model for spatial transcriptomics prediction. MSFF-ST first uses a pathology foundation model to extract features from whole-slide images (WSIs), then fuses features from spatially adjacent and morphologically similar spot regions, and finally incorporates hierarchical WSI-level context via cross-scale attention. Evaluated on three cancer datasets spanning two spatial transcriptomics platforms, MSFF-ST achieves superior performance and generalizability over state-of-the-art methods. Our source code is publicly available at https://github.com/chenw-u/MSFF-ST

The molecular classification of adult-type diffuse gliomas is essential for determining appropriate therapeutic strategies, but genomic sequencing remains costly. Recent advances in digital pathology and deep learning have led to several studies exploring molecular classification using multiple instance learning (MIL) on whole slide images (WSIs). However, achieving optimal classification performance using only histological slides is challenging due to the lack of guidance from genomic data. In this study, we propose a teacher-student distillation framework for glioma molecular classification using WSIs. Our method leverages a pretrained self-normalizing neural network (SNN) as the genomic teacher model, which selects genes based on survival analysis-driven criteria to guide the MIL-based student model in learning effective histological representations. During training, both genomic and pathological data are utilized, while inference relies solely on WSIs. Experimental validation on the TCGA GBM-LGG datasets shows that our approach outperforms state-of-the-art (SOTA) MIL models, highlighting its effectiveness in glioma diagnostic subtyping using WSIs.

The convolutional neural network (CNN) and transformer have gained significant attention in the field of single image super-resolution (SISR), owing to their powerful capacity in nonlinear feature extraction. Nonetheless, these two types of approaches hold their own limitations. For instance, the interaction between convolutional kernels and image content is agnostic in CNN, while the computational complexity increases quadratically along with the spatial resolution in the transformer. To address these concerns, in this article, we propose a novel unified framework named transformer and convolution coupled contrastive network (TC3Net) for SISR, which holds a triple-branch structure to integrate the merits of both CNN and transformer. The proposed TC3Net is mainly composed of several stacked CNN feature extraction (CFE) blocks, transformer feature extraction (TFE) blocks, and coupled contrastive blocks (CCBs) for diverse feature extraction. Particularly, the CCB that consists of the coupled attention block (CAB) and the local-global feature extraction (LGFE) block is designed to fuse feature maps and extract coupled information for better image reconstruction. Moreover, a contrastive loss between the transformer and CNN feature maps is further introduced to enhance their discriminative characteristics and complement the fused features. Experimental results demonstrate that TC3Net outperforms several state-of-the-art (SOTA) methods in the aspect of achieving a better balance between model size and performance.

Continuity of care has been shown to possess numerous health benefits for asthma. However, the use of methodology to predetermine the level of care continuity has been underinvestigated. We recently built a machine learning model to predict the continuity of care for diagnosed asthma patients at the University of Washington Medicine. As there is a consensus on the un-explainable featured nowadays by black-box machine learning, which impedes the clinical deployment of our model. To tackle this issue, we proposed a reliability-constrained association rule mining method, called RC-ARM, to automatically explain the predictions of any machine learning model. First, we introduced the belief function to build a reliability-constrained rule framework. Then, the two-step reliable association rule mining algorithms were developed to generate reliable explanation rules for the machine learning model’s predictions. At last, clinical intervention suggestions regarding each mined rule were embedded for further understanding. The results showed that the proposed method could explain all (110/110) predictions of our machine learning model for asthma patients with low levels of continuity of care. This semantic-fused method sheds light on black-box models and encourages clinical experts to embrace the benefits of machine learning without any prior concern about its lack of explainability.

Bayesian neural networks are powerful inference methods by accounting for randomness in the data and the network model. Uncertainty quantification at the output of neural networks is critical, especially for applications such as autonomous driving and hazardous weather forecasting. However, approaches for theoretical analysis of Bayesian neural networks remain limited. This paper makes a step forward towards mathematical quantification of uncertainty in neural network models and proposes a cubature-rule-based computationally-efficient uncertainty quantification approach that captures layer-wise uncertainties of Bayesian neural networks. The proposed approach approximates the first two moments of the posterior distribution of the parameters by propagating cubature points across the network nonlinearities. Simulation results show that the proposed approach can achieve more diverse layer-wise uncertainty quantification results of neural networks with a fast convergence rate.