Searcharxiv⌕ Search

arXiv subjects

Miaomiao Zhang

Publications and source records attributed to Miaomiao Zhang.

At least 19 recordsLinked to original sources
arXiv2026

GenStrain: Multi-Conditional Generative Diffusion Model To Synthesize High-quality Motion and Strain Maps From Cardiac Magnetic Resonance Images

This paper presents GenStrain, a novel method for synthesizing high-quality cardiac motion-based strain maps from routinely collected cine cardiac magnetic resonance (CMR) images. Existing approaches typically rely on image registration to estimate motion (a sequence of displacements) from cine CMR images, but cine CMR presents very sparse trackable intramyocardial features, presenting challenges to estimate intramyocardial motion. An advanced imaging technique, Displacement Encoding with Stimulated Echoes (DENSE) CMR, is considered as the gold standard for strain assessment, but it requires additional acquisitions, which are not ideal for efficient clinical workflows. To address this gap, we introduce GenStrain, a novel framework designed to learn the data distribution of highly accurate cardiac motion captured by DENSE. In particular, we develop a diffusion process in motion space, where spatiotemporal continuity is carefully considered. To fully leverage both cine CMR images and their associated motion patterns, we introduce a multi-conditional conditioning strategy that integrates anatomical features and motion representations learned by a spatiotemporal registration model to guide the generation process. Experimental results demonstrate that our proposed model, GenStrain, outperforms existing strain analysis methods based on displacement estimates from both traditional registration approaches and state-of-the-art (SOTA) deep learning models. Our model represents a promising step toward more accurate cardiac function assessment and holds great potential for clinical translation. Our code is publicly available at https://github.com/Rishov-MIA/GenStrain.

eess.IV↗
arXiv2026

Compositional Automata Learning of Timed Synchronous Systems

Active model learning is a crucial technique for automatically constructing formal models of black-box timed systems. However, existing learning approaches typically construct a single monolithic automaton for a timed system with multiple synchronous components, obscuring its component structure and hindering component-level analysis. We propose a compositional learning approach that constructs a deterministic real-time automaton (RTA) for each component with access only to the target system as a whole. Upon termination, the learned RTAs form a network that exhibits exactly the same timed behaviors as the target system. Within the respective resource limits, our implementation terminates successfully on 213 of the 240 (88.8%) randomly generated benchmark instances and all six adapted case-study benchmarks, and requires less learning time than monolithic approaches on some instances.

cs.FL↗
arXiv2026

Identifying the principal mechanism of star formation feedback

Feedback from massive stars regulates the formation of next-generation stars and planets and thus shapes the evolution of galaxies, yet observational evidence capable of determining which mechanism dominates the feedback process has long been lacking. Here,we present a large-scale HI survey toward Orion A$.$ With an unprecedented sensitivity and superior resolution, FAST (Five-hundred-meter Aperture Spherical radio Telescope) reveals a well-defined heart-shaped expanding HI bubble, enshrouding the well-known "Veil" bubble of ionized gas. The photoionization-driven and wind-driven gas expands at 10.9 and 13.8 kilometers per second, respectively, with a kinetic energy ratio of 1:2 assuming a spherical symmetry and a uniform HI-to-H2 ratio. This first direct comparison within a single bubble shows that radiation does not dominate the feedback process as predicted by classical theories; instead, stellar winds contribute a comparable share of the injected kinetic energy.

astro-ph.GA↗
arXiv2026

Hierarchical Dynamics within Molecular Clouds: Type 4 Scaling Relations of Molecular Clouds in the Second Galactic Quadrant

We present a statistical study of the hierarchical structure of molecular clouds in the second quadrant of the Galactic midplane (139.75 deg <= l <= 159.75 deg, -5.25 deg <= b <= 8.25 deg), based on high-sensitivity 12CO and 13CO J = 1-0 data from the Milky Way Imaging Scroll Painting survey. Using DBSCAN and a non-binary Dendrogram algorithm, we identify 2,912 13CO clouds. Type 2 (multicloud, single-tracer) scaling relations show arm-dependent trends: the power-law index of the linewidth-size relation is about 0.32 in the Local Arm and about 0.42 in the Perseus and Outer Arms. The classical Keto-Heyer relation is weak in the Local Arm but present in the Perseus and Outer Arms, with a slope of about 0.3. The virial parameter is anti-correlated with cloud size, mass, and surface density, consistent with previous surveys. Type 4 (single-cloud, single-tracer) scaling relations exhibit stronger internal correlations. The linewidth-size index spans about 0.3-0.8 and correlates positively with sonic Mach number, indicating that the relation is not universal but depends on cloud compressibility. While substructures do not follow the conventional surface-density-based Keto-Heyer relation, a volume-density formulation restores a clear virial scaling, with evolutionary tracks aligning along lines of constant virial parameter. Moreover, the structural complexity of molecular clouds, characterized by the relationship between the number of substructures and the min_delta parameter, correlates with surface density rather than sonic Mach number, contrary to results from turbulence-only magnetohydrodynamic simulations. Together, these results are more consistent with a gravity-regulated hierarchical dynamical picture than with a purely turbulence-supported scenario.

astro-ph.GA↗
arXiv2026

Examining Turbulence in Galactic Molecular Clouds. II. Turbulence Cascade Beyond the Scale of Giant Molecular Clouds

We use $^{12}$CO (J=1--0) data from the MWISP survey to investigate turbulence in a $\sim$kpc-scale segment of the Local Arm. By slicing the position-position-velocity cube into narrow layers that follow the Galactic-rotation trend in the $L$-$V$ diagram, we find that the structure functions (SFs) and spatial power spectra (SPS) of the $^{12}$CO (J=1--0) line velocity and intensity exhibit consistent scaling behaviors across all layers, demonstrating that the molecular gas forms a single, coherent turbulent field with energy cascading from $\sim 200$~pc down to parsec scales. The SPS power-law slopes of both the intensity and velocity fields approach the values expected from turbulence models. Cloud-to-cloud velocity SFs based on the molecular clouds located in the region follow the same extended self-similarity scaling (ESS) as the pixel-based statistics, indicating that inter-cloud motions are part of the same large-scale turbulent cascade. Together, these results provide direct observational evidence that molecular clouds are not dynamically isolated entities but are embedded within a larger-scale turbulent flow that links galactic dynamics to star formation on cloud scales.

astro-ph.GA↗
arXiv2026

Geodesic-informed Generative Diffusion Model For Topology-preserved Image Video Generation

Generative diffusion models have emerged as a class of powerful techniques for various imaging applications, including but not limited to synthesis, reconstruction, and segmentation. Despite their success, current generative models pose two key limitations. First, they primarily rely on image intensity and texture information, with limited attention to underlying object geometry. As a result, they do not guarantee geometric or topological consistency during the generation process, which is a crucial requirement for high-stakes domains such as computational anatomy, biology, and robotics, where preserving object structure is critical. Second, existing models fail to explicitly learn or represent shape changes in the generative process. Such deformation dynamics remain occluded within network parameters; hence leaving the transformation process uninterpretable and physically uninformed. To address these challenges, we introduce IGG (Image Generation informed by Geodesic dynamics), a novel framework that integrates topology-preserving geodesic principles into the diffusion-based generative process. In contrast to conventional methods that operate in image intensity space, IGG learns and synthesizes diverse samples within geodesic deformation spaces, where geometric object changes are learned as smooth and invertible smooth mappings from a given template/source image. Our code is publicly available at https://github.com/nellie689/IGG.

cs.LG↗
arXiv2026

FoRIS: Progressive Foreground Refinement for Training-Free In-Context Segmentation

In-Context Segmentation (ICS) aims to precisely segment arbitrary semantic concepts, such as objects or parts, given one or a few annotated visual exemplars. In this paper, we revisit ICS from a more classical segmentation perspective, viewing it as a coarse-to-fine progressive refinement process. Rather than directly predicting the final mask through reference-query matching, we progressively refine the segmentation from coarse and ambiguous foreground responses to precise and complete foreground structures. Building upon this perspective, we propose a training-free in-context segmentation framework, termed FoRIS. Specifically, FoRIS consists of three key stages: Foreground Purification, Foreground Localization, and Foreground Consolidation, which progressively suppress background distractions, localize discriminative target regions, and recover complete foreground structures through semantic aggregation. Experimental results demonstrate that FoRIS achieves SOTA performance across semantic and part segmentation tasks, with average improvements of 4.5 and 4.8 mIoU points over existing approaches in the 1-shot and 5-shot settings, respectively. Code: https://github.com/Xi-Mu-Yu/FoRIS.

cs.CV↗
arXiv2026

Learning To Focus: Anatomy-Guided Attention Regularization for Medical Image Classification

Medical image classification models are ideally expected to identify diagnostically relevant regions while making predictions, yet standard classification losses rarely provide spatial supervision. Explicit supervision via anatomical shape information, such as segmentation masks of task-relevant anatomy, has been shown to guide the network toward regions relevant to the target prediction. However, obtaining such masks incurs substantial manual annotation effort and computational overhead. With the advent of segmentation foundation models that exhibit strong localization of anatomical structures across diverse imaging modalities, we leverage this capability to extract anatomical shape priors without the burden of training a dedicated segmentation model. In this paper, we propose a new framework, Locus, an anatomical attention regularization framework that leverages pretrained segmentation foundation models to guide a classifier's attention toward diagnostically meaningful anatomical structures across diverse imaging modalities. Instead of enforcing pixel-wise alignment with the foundation-model-derived mask, we introduce a regularization term that adaptively balances attention between anatomical (foreground) and background regions, penalizing the classifier when background attention dominates. We validate Locus on eight diverse medical imaging datasets spanning dermoscopy, X-ray, histopathology, and cardiac MRI, showing consistent gains in classification performance alongside improved anatomically grounded attention.

cs.CV↗
arXiv2026

CalcSeg: Confidence-aware 3D Latent Context Curriculum Learning For Myocardial Scar Segmentation From Single-Stack LGE-CMRs

Myocardial scar segmentation from single-stack late gadolinium-enhanced cardiac magnetic resonance (LGE-CMR) imaging has been a longstanding and clinically important challenge, particularly in the presence of low tissue contrast, diffuse, and small scar regions. These challenges are further intensified by the limited availability of 3D spatial context. This paper presents CalcSeg, a Confidence-aware latent context curriculum learning framework that leverages fused 3D feature representations from single-stack 2D LGE-CMR images for robust scar segmentation. Specifically, we introduce a dynamic semi-supervised curriculum learning strategy that progressively expands training from easier to more challenging scar cases using a learned confidence-aware scoring function. Such a function integrates errors in the predicted scar maps with quantified epistemic uncertainty and scar burden estimation to automatically assess sample difficulty without requiring manual labels. To compensate for the limited spatial context in single-stack acquisitions, we then develop a latent slice-wise self-attention to capture inter-slice dependencies and infer 3D spatial representations from sparse 2D inputs. We evaluate CalcSeg on multi-center clinical LGE-CMR datasets and benchmark against existing scar segmentation networks. Experimental results show that CalcSeg consistently outperforms all competing methods, particularly with substantial improvements on clinically challenging cases. Our code is released on Github.

cs.CV↗
arXiv2026

4DLoG: Generative Modeling of Neurodegenerative Brain Anatomy with 4D Longitudinal Diffusion Model

Modeling and predicting neurodegenerative disease progression from medical images remains a major challenge in medical AI, with significant implications for early diagnosis, disease monitoring, and treatment planning. However, most longitudinal neuroimaging datasets are temporally sparse, with substantial gaps and missing follow-up scans for individual subjects. This makes it difficult to learn and accurately capture the continuous anatomical changes associated with disease progression at the level of individual subjects. To address this problem, we propose a novel model named 4DLoG, a full 4D (3DxT) Longitudinal Generative framework that effectively models and synthesizes follow-up brain anatomy over time, conditioned on available clinical and demographic variables. In contrast to previous approaches, our 4DLoG features two main contributions. First, it introduces a full 4D generative diffusion framework that jointly models spatial and temporal dependencies across complete longitudinal sequences through dedicated spatiotemporal attention, with robust spatial patch extraction and temporal alignment. Second, it explicitly learns the distribution of topology-preserving spatiotemporal deformations, which captures realistic geometric changes in brain structures over time. These new components enable a better generation of anatomically plausible future states from an imaging scan at any time point, providing greater flexibility for modeling individual longitudinal brain trajectories. We validate our model through both synthetic sequence generation and downstream longitudinal disease classification, as well as brain segmentation. Experiments on two large-scale longitudinal neuroimage datasets demonstrate that our method outperforms state-of-the-art baselines in generating anatomically accurate, temporally consistent, and clinically meaningful brain trajectories. Our code is available on Github

cs.CV↗
arXiv2026

Generative Brownian Bridge Diffusion In Motion Space For Enhanced Myocardial Strain Analysis

Myocardial strain analysis of cardiac magnetic resonance (CMR) images provides an important tool for evaluating cardiac function. However, current techniques require either human-adjusted post-processing with suboptimal regional accuracy, or specialized acquisitions with limited availability. In this paper, we propose to leverage the power of generative models to synthesize high-quality motion-derived strain values from routinely acquired CMR sequences. Specifically, we develop a novel Brownian bridge diffusion model in motion space to learn the probabilistic mapping between standard CMR motion estimated from widely adopted registration methods and highly accurate motion provided by advanced strain imaging techniques. To promote the fidelity of anatomical structure in the generation process, our model is conditioned on the corresponding CMR images. We validate our method on large-scale multi-center CMR datasets including subjects of paired standard cine CMR and advanced strain imaging acquisitions. Experimental results demonstrate that our framework significantly improves the accuracy of motion prediction and strain analysis from standard CMRs compared to existing learning-based approaches. Our research represents a new paradigm for potentially developing cost-effective, clinically deployable AI tools for cardiac function assessment with enhanced strain accuracy in busy clinical workflows. Our code is publicly available at https://github.com/Rishov-MIA/Brownian-Bridge-strain-analysis.

cs.CV↗
arXiv2026

WhereEdit: Mask-aware Local Latent Editing for One-Step Image Editing

Recent one-step text-to-image (T2I) models enable efficient image synthesis and provide new opportunities for real-time image editing. However, existing one-step editing methods primarily rely on text conditioning for semantic transformation, lacking explicit spatial control over \textit{where} to edit. More importantly, even when spatial constraints are introduced, these methods often struggle to achieve strong and stable semantic modifications within the target regions. In this work, we revisit one-step image editing from a spatially controlled perspective and identify two key challenges: discovering editable regions and achieving effective localized semantic transformation. We reveal that existing methods perform global semantic transport, which limits high-intensity local editing under the one-step setting. To address this issue, we propose \textbf{WhereEdit}, a framework that reformulates one-step editing as localized adaptive editing. WhereEdit automatically identifies semantically relevant regions from internal model features and applies adaptive local modulation to enhance target-region editing while preserving non-target areas and structural consistency. Experiments on the PIE-Bench benchmark demonstrate that WhereEdit consistently outperforms existing one-step image editing methods, achieving superior editing quality while maintaining the efficiency of one-step generation. Additional experiments with region-level supervision further highlight the importance of explicit spatial reasoning for high-quality one-step image editing.

cs.CV↗
arXiv2026

Anatomically Guided Latent Diffusion for Brain MRI Progression Modeling

Accurately modeling longitudinal brain MRI progression is crucial for understanding neurodegenerative diseases and predicting individualized structural changes. Existing state-of-the-art approaches, such as Brain Latent Progression (BrLP), often use multi-stage training pipelines with auxiliary conditioning modules but suffer from architectural complexity, suboptimal use of conditional clinical covariates, and limited guarantees of anatomical consistency. We propose Anatomically Guided Latent Diffusion Model (AG-LDM), a segmentation-guided framework that enforces anatomically consistent progression while substantially simplifying the training pipeline. AG-LDM conditions latent diffusion by directly fusing baseline anatomy, noisy follow-up states, and clinical covariates at the input level, a strategy that avoids auxiliary control networks by learning a unified, end-to-end model that represents both anatomy and progression. A lightweight 3D tissue segmentation model (WarpSeg) provides explicit anatomical supervision during both autoencoder fine-tuning and diffusion model training, ensuring consistent brain tissue boundaries and morphometric fidelity. Experiments on 31,713 ADNI longitudinal pairs and zero-shot evaluation on OASIS-3 demonstrate that AG-LDM matches or surpasses more complex diffusion models, achieving highly competitive image quality and 15-20% reduction in volumetric errors in generated images. AG-LDM also exhibits markedly stronger utilization of temporal and clinical covariates (3.5-31.5x higher covariate sensitivity than BrLP) and generates biologically plausible counterfactual trajectories, accurately capturing hallmarks of Alzheimer's progression such as limbic atrophy and ventricular expansion. These results highlight AG-LDM as an efficient, anatomically grounded framework for reliable brain MRI progression modeling.

cs.CV↗
arXiv2026

Learning to Unify Deformable Shape and Texture Representations for Cardiac Video Classification

Deformable shape representations have proven to be robust complements to texture features in cardiac image classification, offering geometric priors that are invariant to imaging artifacts and intensity variations. However, existing deep networks perform simple concatenation to combine these distinct feature representations, which neither fully exploits their complementary nature nor learns cross-modal feature dependencies. Furthermore, this results in uniform attention across all timepoints; hence ignoring the varying diagnostic importance across the cardiac phases. In this paper, we propose a novel cardiac video classification model that, for the first time, learns temporal features in an integrated space of deformable shape and image texture representations. In particular, we design a bi-directional cross-attention in the latent space to fuse latent deformable shape and image features, allowing each modality to adaptively weight the other based on spatio-temporal correspondence. In contrast to current methods that apply uniform weighting across all the cardiac phases, our approach learns to dynamically adjust the contributions of shape and texture representations, derived from images, over time. We demonstrate state-of-the-art classification performance on a cine cardiac magnetic resonance (CMR) video dataset, achieving improved interpretability from attention mechanisms that identify diagnostically critical cardiac phases and modality contributions.

cs.CV↗
arXiv2026

A study of the Physical Properties and Star Formation Activity of a Large Sample of Molecular Clouds: I Distances

Accurate distances to molecular clouds are crucial for determining their physical properties, understanding star formation, and tracing Galactic spiral structure. A number of 103,517 molecular clouds has been identified by the DBSCAN algorithm in the MWISP Phase I CO survey (l = 9.75-229.75 deg, |b| <= 5.25 deg), most of which lack reliable distances. In this work, we propose three independent methods, all of which match the molecular cloud's velocity-integrated intensity maps of 12CO lines from the MWISP with the three-dimensional dust extinction maps derived from Gaia, Pan-STARRS 1, and 2MASS, to determine molecular cloud distances. We present a catalog of 1,573 molecular clouds with robust distances ranging from approximately 150 pc to 3000 pc, 90 percent of which are measured for the first time, with typical statistical and systematic uncertainties of approximately 20% and 10%, respectively. We also derive their physical properties, such as their mass and sizes. This publicly available catalog of molecular clouds with distances provides a foundation for testing molecular cloud scaling relations and probing how cloud conditions influence star formation across diverse Galactic environments.

astro-ph.GA↗
arXiv2026

Distances to molecular clouds in the Galactic longitude l=10-20 deg from the MWISP 12CO 1-0 survey

We present distances to 56 molecular clouds within $10\degr \leq l \leq 20\degr$ and $|b| \leq 5.25\degr$ from the Milky Way Imaging Scroll Painting (MWISP) $^{12}$CO survey, 47 of which are first-time determinations. The molecular clouds were identified using the DBSCAN algorithm, and their distances were measured with the model-calibrated color-distance method using $J-K{_s}$ colors and the distances provided by 2MASS and \textit{Gaia} EDR3. The distances range from $\sim$275 pc to $\sim$2118 pc. We also derived the physical properties of molecular clouds and found a moderate correlation between the dust extinction and the $^{12}$CO integrated intensity.

astro-ph.GA↗
arXiv2026

Curriculum-Guided Myocardial Scar Segmentation for Ischemic and Non-ischemic Cardiomyopathy

Identification and quantification of myocardial scar is important for diagnosis and prognosis of cardiovascular diseases. However, reliable scar segmentation from Late Gadolinium Enhancement Cardiac Magnetic Resonance (LGE-CMR) images remains a challenge due to variations in contrast enhancement across patients, suboptimal imaging conditions such as post contrast washout, and inconsistencies in ground truth annotations on diffuse scars caused by inter observer variability. In this work, we propose a curriculum learning-based framework designed to improve segmentation performance under these challenging conditions. The method introduces a progressive training strategy that guides the model from high-confidence, clearly defined scar regions to low confidence or visually ambiguous samples with limited scar burden. By structuring the learning process in this manner, the network develops robustness to uncertain labels and subtle scar appearances that are often underrepresented in conventional training pipelines. Experimental results show that the proposed approach enhances segmentation accuracy and consistency, particularly for cases with minimal or diffuse scar, outperforming standard training baselines. This strategy provides a principled way to leverage imperfect data for improved myocardial scar quantification in clinical applications. Our code is publicly available on GitHub.

cs.CV↗
arXiv2026

WFST Astrometric Calibration -- I. Modeling Global Geometric Distortion with Zernike Polynomials

Accurate modeling of geometric distortion is essential for precise astrometric calibration in wide-field imaging surveys. We present a self-calibration method based on Zernike polynomials, applied to imaging data from the Wide Field Survey Telescope (WFST). Our approach constructs a global geometric distortion (GD) model from the position offsets of stars in the WFST r-band relative to Gaia DR3, achieving a median systematic uncertainty of below 10 mas for individual exposures. The correspondence between Zernike polynomials and optical aberrations reveals that the global GD of WFST is dominated by coma, inherent to the optical design, while rapid variations are likely attributed to the atmospheric dispersion corrector. Applying this method to 82 exposures from a single night (20250218), we find that the relative positions of the WFST CCDs remain stable, with standard deviations of less than 0.1 pixel in translation and 1.8 arcsec in rotation. The corrected WFST astrometric system is thereby tied to the Gaia DR3 coordinate frame, with further refinements to be presented in future work.

astro-ph.IM↗