Precision oncology relies on the accurate characterization of spatial molecular distributions in cancer tissues to uncover critical biomarkers and guide clinical decision-making. However, the high dimensionality and complexity of mass spectrometry imaging (MSI) data pose significant challenges for effective analysis. This study presents an unsupervised manifold learning framework to address these challenges by mapping high-dimensional MSI data into a low-dimensional space while preserving essential molecular patterns. This method enables efficient dimensionality reduction, clustering, and visualization of MSI data, facilitating the discovery of spatially resolved molecular features. Applied to datasets from prostate cancer and colorectal adenocarcinoma, the proposed method accurately identifies cancerous regions and reveals highly correlated molecular markers with Pearson correlation coefficients up to 0.79. These findings demonstrate the potential of unsupervised manifold learning to enhance the interpretability and utility of MSI data in precision oncology, paving the way for improved biomarker discovery and cancer diagnostics.

Robust analysis of 3D point cloud data is essential for high-precision applications such as autonomous driving and industrial automation, where models need to consistently perform under complex and unpredictable real-world conditions. Current strategies, including data augmentation techniques and robust network designs, often struggle to effectively capture dynamic disturbances or accommodate the spatial variations of point clouds, thus lacking the required flexibility across diverse environments. To overcome these limitations, we propose a novel methodology to improve the robustness of 3D point cloud processing systems. Our approach simulates generalized corrupted input samples during training, using Radial Basis Functions (RBF) to model smooth deformations based on the control points. These deformations are applied selectively to different regions of the point cloud, adapting to spatial heterogeneity based on local density and geometric complexity. While generating these samples, we employ a combined adversarial loss that simultaneously induces model errors and maximizes the difference in internal feature distributions between the original and perturbed data. Additionally, we introduce a sub-network for distortion-guided feature augmentation to enhance important features while suppressing unreliable ones. This sub-network estimates distortion levels by compressing features and identifying discrepancies, then adjusts feature extraction process accordingly. Experimental results demonstrate that our method outperforms existing approaches on both Computer-Aided Design (CAD) models and real-world LiDAR datasets, enhancing model resilience and accuracy in handling diverse 3D scenarios.

In this paper, we propose one novel model for point cloud semantic segmentation,which exploits both the local and global structures within the point cloud based onthe contextual point representations. Specifically, we enrich each point represen-tation by performing one novel gated fusion on the point itself and its contextualpoints. Afterwards, based on the enriched representation, we propose one novelgraph pointnet module, relying on the graph attention block to dynamically com-pose and update each point representation within the local point cloud structure.Finally, we resort to the spatial-wise and channel-wise attention strategies to exploitthe point cloud global structure and thereby yield the resulting semantic label foreach point. Extensive results on the public point cloud databases, namely theS3DIS and ScanNet datasets, demonstrate the effectiveness of our proposed model,outperforming the state-of-the-art approaches. Our code for this paper is available at https://github.com/fly519/ELGS.