arXiv subjects

Xingyi Guo

Publications and source records attributed to Xingyi Guo.

3 recordsLinked to original sources

arXiv2026

One cluster, two clusters, no cluster? -- The curious case of HSC 2686 and Lynga 3

This study was motivated by the search for Planetary Nebulae (PNe) and Open Cluster (OCs) associations. Only 5 PNe are currently in Galactic OCs among the previous $\sim4,800$ OCs known. They are valuable because as cluster members their properties can be linked to their progenitor stars, something not possible for general field PNe. Since the GAIA astrometric satellite first data release, thousands of new OC candidates have been identified. This offers fresh motivation to search them as hosts for known PNe. Statistically we expect 30 OC-PN pairs to be present. We searched these GAIA candidate OCs, found by Hunt \& Reffert (2023), for known central stars of PNe (CSPNe) as a practical proxy for the PNe themselves and one suited to automated searches. We identified 1 potential CSPN (IRAS 15127-5811) of "Likely" PN [GKF2010] MN18 in the HASH database, as a claimed member of the putative cluster HSC~2686. A second cluster (Lynga~3) with similar parameters and in very close angular proximity could also be a potential host. Here we reclassify CSPN IRAS 15127-5811 as a blue supergiant with a surrounding bi-polar nebula. Nevertheless, we collected all available information on proposed member stars for both these clusters. We conclude that neither claimed cluster is real. Our results shed considerable doubt on the veracity of many newly identified OCs that have modest numbers of stellar members.

astro-ph.SR↗

arXiv2026

Explainable Pathomics Feature Visualization via Correlation-aware Conditional Feature Editing

Pathomics is a recent approach that offers rich quantitative features beyond what black-box deep learning can provide, supporting more reproducible and explainable biomarkers in digital pathology. However, many derived features (e.g., "second-order moment") remain difficult to interpret, especially across different clinical contexts, which limits their practical adoption. Conditional diffusion models show promise for explainability through feature editing, but they typically assume feature independence**--**an assumption violated by intrinsically correlated pathomics features. Consequently, editing one feature while fixing others can push the model off the biological manifold and produce unrealistic artifacts. To address this, we propose a Manifold-Aware Diffusion (MAD) framework for controllable and biologically plausible cell nuclei editing. Unlike existing approaches, our method regularizes feature trajectories within a disentangled latent space learned by a variational auto-encoder (VAE). This ensures that manipulating a target feature automatically adjusts correlated attributes to remain within the learned distribution of real cells. These optimized features then guide a conditional diffusion model to synthesize high-fidelity images. Experiments demonstrate that our approach is able to navigate the manifold of pathomics features when editing those features. The proposed method outperforms baseline methods in conditional feature editing while preserving structural coherence.

cs.CV↗

arXiv2025

HistoWAS: A Pathomics Framework for Large-Scale Feature-Wide Association Studies of Tissue Topology and Patient Outcomes

High-throughput "pathomic" analysis of Whole Slide Images (WSIs) offers new opportunities to study tissue characteristics and for biomarker discovery. However, the clinical relevance of the tissue characteristics at the micro- and macro-environment level is limited by the lack of tools that facilitate the measurement of the spatial interaction of individual structure characteristics and their association with clinical parameters. To address these challenges, we introduce HistoWAS (Histology-Wide Association Study), a computational framework designed to link tissue spatial organization to clinical outcomes. Specifically, HistoWAS implements (1) a feature space that augments conventional metrics with 30 topological and spatial features, adapted from Geographic Information Systems (GIS) point pattern analysis, to quantify tissue micro-architecture; and (2) an association study engine, inspired by Phenome-Wide Association Studies (PheWAS), that performs mass univariate regression for each feature with statistical correction. As a proof of concept, we applied HistoWAS to analyze a total of 102 features (72 conventional object-level features and our 30 spatial features) using 385 PAS-stained WSIs from 206 participants in the Kidney Precision Medicine Project (KPMP). The code and data have been released to https://github.com/hrlblab/histoWAS.

cs.CV↗