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Ye Luo

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9 papers

2 author rows

AAAI Conference 2026 Conference Paper

Learning Latent Imaging Biomarkers for Interpretable Microvascular Invasion Prediction in Hepatocellular Carcinoma

Ji Rao
Xinyu Liu
Yong Yi
Ying Xiao
Ye Luo

Microvascular invasion (MVI) is a critical prognostic factor that significantly impacts postoperative outcomes in hepatocellular carcinoma (HCC). As the current gold standard for the diagnosis of MVI is based on the postoperative histopathological examination of whole slide images, accurate preoperative prediction of MVI status using magnetic resonance imaging (MRI) presents both a substantial clinical imperative and a significant challenge. In order to discover reliable MRI-based imaging biomarkers to support clinical decision making and enhance the interpretability of deep learning-based diagnostic models, we propose a novel interpretable MVI prediction framework in which the shared latent visual attributes are first learned and then used for potential imaging biomarker extraction and MVI diagnosis, respectively. To ensure that the visual attributes of these biomarkers are generalizable across diverse patients, the similarity constraints at the intra-patient level and the inter-patient level are enforced within the learned feature space, enabling intuitive biomarker discovery directly from the original image space. To guarantee semantic alignment between biomarkers and the characteristics of individual patients, we introduce a novel classification mechanism that directly links the alignment between each biomarker and patient-specific characteristics with the prediction, thereby ensuring a precise prediction of MVI. Furthermore, the interpretability of the model is enhanced by integrating a mask-based visual explanation method that highlights regions in patient images that correspond to the identified biomarkers. Extensive experiments on two MVI prediction datasets: HCC-WCH and HCC-ZSH unequivocally demonstrate our method's superior performance in both classification accuracy and interpretability.

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AAAI Conference 2026 Conference Paper

Stepwise Contrastive Reasoning for Retrieval-Augmented Generation over Knowledge Graphs

Chenxiao Lin
Ye Luo
KunHong Liu
Qingqiang Wu

Retrieval-augmented generation (RAG) enhances the reasoning capabilities of large language models (LLMs) by incorporating external knowledge. Among available sources, knowledge graphs (KGs) offer a structured and reliable foundation for factual information, making them increasingly popular in efforts to improve reasoning faithfulness in RAG. Most existing KG-based RAG methods rely on LLMs to extract knowledge from KGs. However, these approaches often require costly fine-tuning and struggle to navigate deep graph structures, limiting their effectiveness in multi-hop reasoning tasks. To address these challenges, we propose Stepwise Contrastive Reasoning (SCR), a lightweight framework that integrates graph structure and textual context for efficient and interpretable RAG over KGs. SCR combines relational message passing layers to encode KG entities with a Transformer encoder for processing question text. It decomposes reasoning into a series of alignment steps. At each step, SCR compares the current topic entity and its neighbors with the question representation, selecting the most relevant entity as the next topic entity. The question is then updated with this entity's textual description. This process continues until the selected entity no longer changes, indicating that the answer entity has been reached. Through stepwise alignment, SCR enables compact models to perform faithful and interpretable reasoning over large-scale KGs. Extensive experiments on several widely used KGQA benchmarks demonstrate that SCR not only achieves state-of-the-art performance but also effectively boosts the capabilities of smaller language models to match those of LLMs.

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JBHI Journal 2025 Journal Article

Bilateral Proxy Federated Domain Generalization for Privacy-Preserving Medical Image Diagnosis

Huilin Lai
Ye Luo
Bo Li
Jianwei Lu
Junsong Yuan

Contemporary domain generalization methods have demonstrated effectiveness in aiding the generalized diagnosis of medical images with multi-source data by joint optimization. However, the centralized training paradigm employed by these approaches becomes infeasible when data are non-shared across domains due to the high privacy of medical data. Despite attempts by existing federated domain generalization methods to address this issue, the simultaneous attainment of strict privacy protection and a satisfactory level of generalization ability on out-of-distribution data remains a persistent challenge. In this paper, to tackle this challenging problem, we propose a novel approach called the Bilateral Proxy Framework (BPF). The BPF leverages the client-side proxies to facilitate the strict privacy-preserving communications with the server and ensure smoother and more stable convergences of local models through mutual distillation. Meanwhile, the server-side proxy adopts a distance-based strategy and a parameter moving average scheme, which enhances the stability and robustness of the global model, particularly by averting abrupt parameter changes that could result in fluctuations or overfitting. Through these advancements, our framework strives to enhance the generalization capability of the global model, enabling more accurate and reliable medical image diagnosis in federated settings. The effectiveness of our method is demonstrated with superior performance over state-of-the-arts on both simulated and real-world distribution medical image diagnosis tasks.

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JMLR Journal 2025 Journal Article

High-Dimensional L2-Boosting: Rate of Convergence

Ye Luo
Martin Spindler
Jannis Kueck

Boosting is one of the most significant developments in machine learning. This paper studies the rate of convergence of L2-Boosting in a high-dimensional setting under early stopping. We close a gap in the literature and provide the rate of convergence of L2-Boosting in a high-dimensional setting under approximate sparsity and without beta-min condition. We also show that the rate of convergence of the classical L2-Boosting depends on the design matrix described by a sparse eigenvalue condition. To show the latter results, we derive new, improved approximation results for the pure greedy algorithm, based on analyzing the revisiting behavior of L2-Boosting. These results might be of independent interest. Moreover, we introduce so-called "restricted" L2-Boosting. The restricted L2-Boosting algorithm sticks to the set of the previously chosen variables, exploits the information contained in these variables first and then only occasionally allows to add new variables to this set. We derive the rate of convergence for restricted L2-Boosting under early stopping which is close to the convergence rate of Lasso in an approximate sparse, high-dimensional setting without beta-min condition. We also introduce feasible rules for early stopping, which can be easily implemented and used in applied work. Finally, we present simulation studies to illustrate the relevance of our theoretical results and to provide insights into the practical aspects of boosting. In these simulation studies, L2-Boosting clearly outperforms Lasso. An empirical illustration and the proofs are contained in the Appendix. [abs] [ pdf ][ bib ] &copy JMLR 2025. ( edit, beta )

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AAAI Conference 2025 Conference Paper

SSC-VAE: Structured Sparse Coding Based Variational Autoencoder for Detail Preserved Image Reconstruction

Hao Wang
Lu Wang
Zhongyu Wang
Lixin Ma
Ye Luo

Discrete latent representation techniques, such as Vector Quantization (VQ) and Sparse Coding (SC), have demonstrated superior image reconstruction and generation quality compared to continuous representation methods in Variational Autoencoders (VAEs). However, existing approaches often treat the latent representations of an image independently in their discrete representation space, neglecting both the inherent structural information within each representation and the correlations among them. This oversight leads to coarse representations and suboptimal generated results. In this paper, we address these limitations by introducing correlations among and within the latent representations of individual images in the latent discrete space of VAEs using sparse coding. We impose two-dimensional structural information through adaptive thresholding, enhancing local structure in image representations while suppressing noise via parsimonious representation with a learned dictionary. Empirical studies on three real benchmark datasets, including a clinical Ultrasound dataset, BSDS500, and mini-Imagenet, demonstrate that our proposed model preserves fine-grained details in image reconstruction and significantly outperforms baseline models of SC-VAE and VQ-VAE across objective and subjective image quality metrics. Particularly noteworthy are the substantial performance improvements observed on the ultrasound dataset, where structure information is crucial. Specifically, we observe significant performance improvements of 7.68 % and 17.03 % in SSIM, 3.25 dB and 6.58 dB in PSNR, 0.15 and 0.24 in LPIPS, 45.38 and 84.05 in FID over SC-VAE and VQ-VAE, respectively, indicating the superiority of our method in terms of image reconstruction quality and fidelity.

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ECAI Conference 2025 Conference Paper

Tropical Algebra Meets Quantization: Less Multiplication QTCNNs for Efficient Inference

Mingbo Li
Chang Dong
Ye Luo

The escalating computational demands of deep learning models pose significant challenges for deployment on resource-constrained devices. This paper introduces a novel optimization framework that synergizes tropical algebra with neural network quantization to achieve substantial computational efficiency gains in Tropical Convolutional Neural Networks (TCNNs). The structural optimization of tropical algebra’s unique ability to replace multiplication operations with addition in TCNNs, is further enhanced by applying advanced parameter quantization techniques, including Post-Training Quantization (PTQ), Quantization-Aware Training (QAT), Learned Step-Size Quantization (LSQ), and DoReFa quantization. Through extensive experiments on CIFAR-10 and SVHN datasets, we demonstrate that our Quantized Tropical Convolutional Neural Networks (QTCNNs), based on ResNet18/34 architectures, achieve competitive or superior performance compared to both quantized standard CNNs and full-precision networks, while substantially reducing storage requirements and computational costs. Our systematic analysis reveals that combining tropical algebra with quantization creates synergistic optimization effects that neither approach can achieve independently, offering a promising direction for efficient deep learning deployment in resource-constrained environments. Code is available at https: //github. com/luoye-group/QTCNNs.

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JBHI Journal 2023 Journal Article

Domain-Aware Dual Attention for Generalized Medical Image Segmentation on Unseen Domains

Huilin Lai
Ye Luo
Bo Li
Guokai Zhang
Jianwei Lu

Recently, there has been significant progress in medical image segmentation utilizing deep learning techniques. However, these achievements largely rely on the supposition that the source and target domain data are identically distributed, and the direct application of related methods without addressing the distribution shift results in dramatic degradation in realistic clinical environments. Current approaches concerning the distribution shift either require the target domain data in advance for adaptation, or focus only on the distribution shift across domains while ignoring the intra-domain data variation. This paper proposes a domain-aware dual attention network for the generalized medical image segmentation task on unseen target domains. To alleviate the severe distribution shift between the source and target domains, an Extrinsic Attention (EA) module is designed to learn image features with knowledge originating from multi-source domains. Moreover, an Intrinsic Attention (IA) module is also proposed to handle the intra-domain variation by individually modeling the pixel-region relations derived from an image. The EA and IA modules complement each other well in terms of modeling the extrinsic and intrinsic domain relationships, respectively. To validate the model effectiveness, comprehensive experiments are conducted on various benchmark datasets, including the prostate segmentation in magnetic resonance imaging (MRI) scans and the optic cup/disc segmentation in fundus images. The experimental results demonstrate that our proposed model effectively generalizes to unseen domains and exceeds the existing advanced approaches.

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JBHI Journal 2022 Journal Article

Cross-Modal Prostate Cancer Segmentation via Self-Attention Distillation

Guokai Zhang
Xiaoang Shen
Yu-Dong Zhang
Ye Luo
Jihao Luo
Dandan Zhu
Hanmei Yang
Weigang Wang

The automatic and accurate segmentation of the prostate cancer from the multi-modal magnetic resonance images is of prime importance for the disease assessment and follow-up treatment plan. However, how to use the multi-modal image features more efficiently is still a challenging problem in the field of medical image segmentation. In this paper, we develop a cross-modal self-attention distillation network by fully exploiting the encoded information of the intermediate layers from different modalities, and the generated attention maps of different modalities enable the model to transfer significant and discriminative information that contains more details. Moreover, a novel spatial correlated feature fusion module is further employed for learning more complementary correlation and non-linear information of different modality images. We evaluate our model in five-fold cross-validation on 358 MRI images with biopsy confirmed. Without bells and whistles, our proposed network achieves state-of-the-art performance on extensive experiments.

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JMLR Journal 2021 Journal Article

Shape-Enforcing Operators for Generic Point and Interval Estimators of Functions

Xi Chen
Victor Chernozhukov
Ivan Fernandez-Val
Scott Kostyshak
Ye Luo

A common problem in econometrics, statistics, and machine learning is to estimate and make inference on functions that satisfy shape restrictions. For example, distribution functions are nondecreasing and range between zero and one, height growth charts are nondecreasing in age, and production functions are nondecreasing and quasi-concave in input quantities. We propose a method to enforce these restrictions ex post on generic unconstrained point and interval estimates of the target function by applying functional operators. The interval estimates could be either frequentist confidence bands or Bayesian credible regions. If an operator has reshaping, invariance, order-preserving, and distance-reducing properties, the shape-enforced point estimates are closer to the target function than the original point estimates and the shape-enforced interval estimates have greater coverage and shorter length than the original interval estimates. We show that these properties hold for six different operators that cover commonly used shape restrictions in practice: range, convexity, monotonicity, monotone convexity, quasi-convexity, and monotone quasi-convexity, with the latter two restrictions being of paramount importance. The main attractive property of the post-processing approach is that it works in conjunction with any generic initial point or interval estimate, obtained using any of parametric, semi-parametric or nonparametric learning methods, including recent methods that are able to exploit either smoothness, sparsity, or other forms of structured parsimony of target functions. The post-processed point and interval estimates automatically inherit and provably improve these properties in finite samples, while also enforcing qualitative shape restrictions brought by scientific reasoning. We illustrate the results with two empirical applications to the estimation of a height growth chart for infants in India and a production function for chemical firms in China. [abs] [ pdf ][ bib ] &copy JMLR 2021. ( edit, beta )

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