Spatial transcriptomics (ST) bridges gene expression and tissue morphology but faces clinical adoption barriers due to technical complexity and prohibitive costs. While computational methods predict gene expression from H&E-stained whole-slide images (WSIs), existing approaches often fail to capture the intricate biological heterogeneity within spots and are susceptible to morphological noise when integrating contextual information from surrounding tissue. To overcome these limitations, we propose HiFusion, a novel deep learning framework that integrates two complementary components. First, we introduce the Hierarchical Intra-Spot Modeling module that extracts fine-grained morphological representations through multi-resolution sub-patch decomposition, guided by a feature alignment loss to ensure semantic consistency across scales. Concurrently, we present the Context-aware Cross-scale Fusion module, which employs cross-attention to selectively incorporate biologically relevant regional context, thereby enhancing representational capacity. This architecture enables comprehensive modeling of both cellular-level features and tissue microenvironmental cues, which are essential for accurate gene expression prediction. Extensive experiments on two benchmark ST datasets demonstrate that HiFusion achieves state-of-the-art performance across both 2D slide-wise cross-validation and more challenging 3D sample-specific scenarios. These results underscore HiFusion’s potential as a robust, accurate, and scalable solution for ST inference from routine histopathology.

Pulmonary diseases rank prominently among the principal causes of death worldwide. Curing them will require, among other things, a better understanding of the complex 3D tree-shaped structures within the pulmonary system, such as airways, arteries, and veins. Traditional approaches using high-resolution image stacks and standard CNNs on dense voxel grids face challenges in computational efficiency, limited resolution, local context, and inadequate preservation of shape topology. Our method addresses these issues by shifting from dense voxel to sparse point representation, offering better memory efficiency and global context utilization. However, the inherent sparsity in point representation can lead to a loss of crucial connectivity in tree-shaped structures. To mitigate this, we introduce graph learning on skeletonized structures, incorporating differentiable feature fusion for improved topology and long-distance context capture. Furthermore, we employ an implicit function for efficient conversion of sparse representations into dense reconstructions end-to-end. The proposed method not only delivers state-of-the-art performance in labeling accuracy, both overall and at key locations, but also enables efficient inference and the generation of closed surface shapes. Addressing data scarcity in this field, we have also curated a comprehensive dataset to validate our approach. Data and code are available at https://github.com/M3DV/pulmonary-tree-labeling.

Alzheimer's disease (AD) is the most common form of dementia in the elderly. As early detection and diagnosis is imperative for the intervention and prevention of its progression into more detrimental stages, pioneering works have been proposed that use the resting-state functional MRI (rs-fMRI) to identify early mild cognitive impairment (EMCI) based on various convolutional neural networks (CNNs). However the accuracy is not satisfactory. In this paper, we propose a multi-view model based on the SlowFast network, a recently proposed model for video recognition. The rs-fMRI data are treated as videos from three perspectives (i.e. coronal, horizontal and sagittal, corresponding to three anatomical planes in human body) and the jointly learned hierarchical representations are fused in the fully connected layer. We examine our model on a publicly accessible Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Our method significantly outperforms other competing methods and achieves state-of-the-art accuracy. Besides, we also provide a baseline on the classification task over all clinical phases of AD.