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Researcher profile

Li Liang

Li Liang contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
math.RTComputer Visionmath.RAeess.IVeess.SYmath.ACmath.KTmath.OCSystems and Control

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Published work

9 published item(s)

preprint2026arXiv

A Breast Vision Pathology Foundation Model for Real-world Clinical Utility

Pathology foundation models have shown strong retrospective performance, but whether such systems can support clinically relevant use remains unclear. This challenge is particularly important in breast cancer, where pathological assessment serves as the gold standard for diagnosis and guides treatment planning, surgical decision-making and risk stratification across pre-, intra- and post-operative stages. Here we present \textbf{BRAVE}, a breast-adaptive pathology foundation model developed and evaluated using a total resource of 101,638 breast whole-slide images from 32 sources across Asia, Europe and North America. We assessed BRAVE across 34 tasks in 82 cohorts spanning pre-operative biopsy, intra-operative frozen section and post-operative resection, using an evidence chain comprising retrospective benchmarking, clinically challenging scenarios, workflow-oriented clinical impact simulations, prospective observational validation with the thresholds locked in the retrospective cohorts and crossover pathologist-AI interaction studies. Across these settings, BRAVE supported practical roles in the clinical workflow, including safe exclusion of low-risk cases from routine review, AI-assisted second-review rescue of initially missed positives and prioritization of cases for further assessment. In prospective validation across three centres, BRAVE excluded 76.9% of negative biopsy cases (NPV 0.953) and 70.1% of negative frozen-section cases (NPV 0.973), and triaged 78.8% of post-operative subtyping cases as high-confidence clear-cut cases (NPV 1.000). In reader studies, AI assistance improved balanced accuracy from 88.5% to 95.1% (OR 3.14, P<0.001), with better efficiency, confidence and inter-rater agreement. BRAVE-derived scores also independently predicted disease-free survival (adjusted HR 4.79, P<0.001) and overall survival (adjusted HR 8.14, P<0.001).

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

CymbaDiff: Structured Spatial Diffusion for Sketch-based 3D Semantic Urban Scene Generation

Outdoor 3D semantic scene generation produces realistic and semantically rich environments for applications such as urban simulation and autonomous driving. However, advances in this direction are constrained by the absence of publicly available, well-annotated datasets. We introduce SketchSem3D, the first large-scale benchmark for generating 3D outdoor semantic scenes from abstract freehand sketches and pseudo-labeled annotations of satellite images. SketchSem3D includes two subsets, Sketch-based SemanticKITTI and Sketch-based KITTI-360 (containing LiDAR voxels along with their corresponding sketches and annotated satellite images), to enable standardized, rigorous, and diverse evaluations. We also propose Cylinder Mamba Diffusion (CymbaDiff) that significantly enhances spatial coherence in outdoor 3D scene generation. CymbaDiff imposes structured spatial ordering, explicitly captures cylindrical continuity and vertical hierarchy, and preserves both physical neighborhood relationships and global context within the generated scenes. Extensive experiments on SketchSem3D demonstrate that CymbaDiff achieves superior semantic consistency, spatial realism, and cross-dataset generalization. The code and dataset will be available at https://github.com/Lillian-research-hub/CymbaDiff

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preprint2026arXiv

Representations of generalized linear Reedy categories and abelian model structures

In this paper we consider representations of generalized $k$-linear Reedy categories $\underline{\mathscr{C}}$, a common generalization of $k$-linear Reedy categories introduced by Georgiois-Št&#39;ov\&#39;ıček and $k$-linearizations of generalized Reedy categories introduced by Berger-Moerdijk, and construct abelian model structures on $\underline{\mathscr{C}} \text{-}\mathrm{Mod}$. In the first part, we show that $\underline{\mathscr{C}}$ can be viewed as an infinite categorical analogue of standardly stratified algebras. Explicitly, we give a parameterization of irreducible representations of $\underline{\mathscr{C}} \text{-}\mathrm{Mod}$, provide several sufficient criteria such that $\underline{\mathscr{C}} \text{-}\mathrm{Mod}$ is equivalent to the Cartesian product of module categories over the ``local&#34; endomorphism algebras of $\underline{\mathscr{C}}$, and describe applications of these results to representation theory of some interesting combinatorial categories including categories of spans and the category of finite dimensional vector spaces over a finite field and linear maps. In the second part, using the technique of Grothendieck bifibrations, we glue a family of complete cotorsion pairs in the module categories of these ``local&#34; endomorphism algebras to a complete cotorsion pair in $\underline{\mathscr{C}} \text{-}\mathrm{Mod}$, and deduce that under certain mild conditions a family of abelian model structures on these ``local&#34; module categories can be glued to an abelian model structure on $\underline{\mathscr{C}} \text{-}\mathrm{Mod}$. As applications, we obtain a few abelian model structures on generalized $k$-linear direct or inverse categories.

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preprint2025arXiv

Value of Multi-pursuer Single-evader Pursuit-evasion Game with Terminal Cost of Evader&#39;s Position: Relaxation of Convexity Condition

In this study, we consider a multi-pursuer single-evader quantitative pursuit-evasion game with payoff function that includes only the terminal cost. The terminal cost is a function related only to the terminal position of the evader. This problem has been extensively studied in target defense games. Here, we prove that a candidate for the value function generated by geometric method is the viscosity solution of the corresponding Hamilton-Jacobi-Isaacs partial differential equation (HJI PDE) Dirichlet problem. Therefore, the value function of the game at each point can be computed by a mathematical program. In our work, the convexity of the terminal cost or the target is not required. The terminal cost only needs to be locally Lipschitz continuous. The cases in which the terminal costs or the targets are not convex are covered. Therefore, our result is more universal than those of previous studies, and the complexity of the proof is improved. We also discuss the optimal strategies in this game and present an intuitive explanation of this value function.

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preprint2022arXiv

A study of Tate homology via the approximation theory with applications to the depth formula

In this paper we are concerned with absolute, relative and Tate Tor modules. In the first part of the paper we generalize a result of Avramov and Martsinkovsky by using the Auslander-Buchweitz approximation theory, and obtain a new exact sequence connecting absolute Tor modules with relative and Tate Tor modules. In the second part of the paper we consider a depth equality, called the depth formula, which has been initially introduced by Auslander and developed further by Huneke and Wiegand. As an application of our main result, we generalize a result of Yassemi and give a new sufficient condition implying the depth formula to hold for modules of finite Gorenstein and finite injective dimension.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

RestainNet: a self-supervised digital re-stainer for stain normalization

Color inconsistency is an inevitable challenge in computational pathology, which generally happens because of stain intensity variations or sections scanned by different scanners. It harms the pathological image analysis methods, especially the learning-based models. A series of approaches have been proposed for stain normalization. However, most of them are lack flexibility in practice. In this paper, we formulated stain normalization as a digital re-staining process and proposed a self-supervised learning model, which is called RestainNet. Our network is regarded as a digital restainer which learns how to re-stain an unstained (grayscale) image. Two digital stains, Hematoxylin (H) and Eosin (E) were extracted from the original image by Beer-Lambert&#39;s Law. We proposed a staining loss to maintain the correctness of stain intensity during the restaining process. Thanks to the self-supervised nature, paired training samples are no longer necessary, which demonstrates great flexibility in practical usage. Our RestainNet outperforms existing approaches and achieves state-of-the-art performance with regard to color correctness and structure preservation. We further conducted experiments on the segmentation and classification tasks and the proposed RestainNet achieved outstanding performance compared with SOTA methods. The self-supervised design allows the network to learn any staining style with no extra effort.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Gorenstein flat representations of left rooted quivers

We study Gorenstein flat objects in the category ${\sf Rep}(Q,R)$ of representations of a left rooted quiver $Q$ with values in ${\sf Mod}(R)$, the category of all left $R$-modules, where $R$ is an arbitrary associative ring. We show that a representation $X$ in ${\sf Rep}(Q,R)$ is Gorenstein flat if and only if for each vertex $i$ the canonical homomorphism $φ_i^X: \oplus_{a:j\to i}X(j)\to X(i)$ is injective, and the left $R$-modules $X(i)$ and ${\rm Coker}φ_i^X$ are Gorenstein flat. As an application of this result, we show that there is a hereditary abelian model structure on ${\sf Rep}(Q,R)$ whose cofibrant objects are precisely the Gorenstein flat representations, fibrant objects are precisely the cotorsion representations, and trivial objects are precisely the representations with values in the right orthogonal category of all projectively coresolved Gorenstein flat left $R$-modules.

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