arXiv subjects

Tianxiang Cui

Publications and source records attributed to Tianxiang Cui.

7 recordsLinked to original sources

arXiv2026

SSMamba: A Self-Supervised Hybrid State Space Model for Pathological Image Classification

Pathological diagnosis is highly reliant on image analysis, where Regions of Interest (ROIs) serve as the primary basis for diagnostic evidence, while whole-slide image (WSI)-level tasks primarily capture aggregated patterns. To extract these critical morphological features, ROI-level Foundation Models (FMs) based on Vision Transformers (ViTs) and large-scale self-supervised learning (SSL) have been widely adopted. However, three core limitations remain in their application to ROI analysis: (1) cross-magnification domain shift, as fixed-scale pretraining hinders adaptation to diverse clinical settings; (2) inadequate local-global relationship modeling, wherein the ViT backbone of FMs suffers from high computational overhead and imprecise local characterization; (3) insufficient fine-grained sensitivity, as traditional self-attention mechanisms tend to overlook subtle diagnostic cues. To address these challenges, we propose SSMamba, a hybrid SSL framework that enables effective fine-grained feature learning without relying on large external datasets. This framework incorporates three domain-adaptive components: Mamba Masked Image Modeling (MAMIM) for mitigating domain shift, a Directional Multi-scale (DMS) module for balanced local-global modeling, and a Local Perception Residual (LPR) module for enhanced fine-grained sensitivity. Employing a two-stage pipeline, SSL pretraining on target ROI datasets followed by supervised fine-tuning (SFT), SSMamba outperforms 11 state-of-the-art (SOTA) pathological FMs on 10 public ROI datasets and surpasses 8 SOTA methods on 6 public WSI datasets. These results validate the superiority of task-specific architectural designs for pathological image analysis.

cs.CV↗

arXiv2025

LGPS: A Lightweight GAN-Based Approach for Polyp Segmentation in Colonoscopy Images

Colorectal cancer (CRC) is a major global cause of cancer-related deaths, with early polyp detection and removal during colonoscopy being crucial for prevention. While deep learning methods have shown promise in polyp segmentation, challenges such as high computational costs, difficulty in segmenting small or low-contrast polyps, and limited generalizability across datasets persist. To address these issues, we propose LGPS, a lightweight GAN-based framework for polyp segmentation. LGPS incorporates three key innovations: (1) a MobileNetV2 backbone enhanced with modified residual blocks and Squeeze-and-Excitation (ResE) modules for efficient feature extraction; (2) Convolutional Conditional Random Fields (ConvCRF) for precise boundary refinement; and (3) a hybrid loss function combining Binary Cross-Entropy, Weighted IoU Loss, and Dice Loss to address class imbalance and enhance segmentation accuracy. LGPS is validated on five benchmark datasets and compared with state-of-the-art(SOTA) methods. On the largest and challenging PolypGen test dataset, LGPS achieves a Dice of 0.7299 and an IoU of 0.7867, outperformed all SOTA works and demonstrating robust generalization. With only 1.07 million parameters, LGPS is 17 times smaller than the smallest existing model, making it highly suitable for real-time clinical applications. Its lightweight design and strong performance underscore its potential for improving early CRC diagnosis. Code is available at https://github.com/Falmi/LGPS/.

cs.CV↗

arXiv2024

End-to-end Generative Spatial-Temporal Ultrasonic Odometry and Mapping Framework

Performing simultaneous localization and mapping (SLAM) in low-visibility conditions, such as environments filled with smoke, dust and transparent objets, has long been a challenging task. Sensors like cameras and Light Detection and Ranging (LiDAR) are significantly limited under these conditions, whereas ultrasonic sensors offer a more robust alternative. However, the low angular resolution, slow update frequency, and limited detection accuracy of ultrasonic sensors present barriers for SLAM. In this work, we propose a novel end-to-end generative ultrasonic SLAM framework. This framework employs a sensor array with overlapping fields of view, leveraging the inherently low angular resolution of ultrasonic sensors to implicitly encode spatial features in conjunction with the robot's motion. Consecutive time frame data is processed through a sliding window mechanism to capture temporal features. The spatiotemporally encoded sensor data is passed through multiple modules to generate dense scan point clouds and robot pose transformations for map construction and odometry. The main contributions of this work include a novel ultrasonic sensor array that spatiotemporally encodes the surrounding environment, and an end-to-end generative SLAM framework that overcomes the inherent defects of ultrasonic sensors. Several real-world experiments demonstrate the feasibility and robustness of the proposed framework.

cs.RO↗

arXiv2024

Market Misconduct in Decentralized Finance (DeFi): Analysis, Regulatory Challenges and Policy Implications

Technological advancement drives financial innovation, reshaping the traditional finance landscape and redefining user-market interactions. The rise of blockchain and Decentralized Finance (DeFi) underscores this intertwined evolution of technology and finance. While DeFi has introduced exciting opportunities, it has also exposed the ecosystem to new forms of market misconduct. This paper aims to bridge the academic and regulatory gaps by addressing key research questions about market misconduct in DeFi. We begin by discussing how blockchain technology can potentially enable the emergence of novel forms of market misconduct. We then offer a comprehensive definition and taxonomy for understanding DeFi market misconduct. Through comparative analysis and empirical measurements, we examine the novel forms of misconduct in DeFi, shedding light on their characteristics and social impact. Subsequently, we investigate the challenges of building a tailored regulatory framework for DeFi. We identify key areas where existing regulatory frameworks may need enhancement. Finally, we discuss potential approaches that bring DeFi into the regulatory perimeter.

q-fin.CP↗

arXiv2024

Fast One-Stage Unsupervised Domain Adaptive Person Search

Unsupervised person search aims to localize a particular target person from a gallery set of scene images without annotations, which is extremely challenging due to the unexpected variations of the unlabeled domains. However, most existing methods dedicate to developing multi-stage models to adapt domain variations while using clustering for iterative model training, which inevitably increases model complexity. To address this issue, we propose a Fast One-stage Unsupervised person Search (FOUS) which complementary integrates domain adaptaion with label adaptaion within an end-to-end manner without iterative clustering. To minimize the domain discrepancy, FOUS introduced an Attention-based Domain Alignment Module (ADAM) which can not only align various domains for both detection and ReID tasks but also construct an attention mechanism to reduce the adverse impacts of low-quality candidates resulting from unsupervised detection. Moreover, to avoid the redundant iterative clustering mode, FOUS adopts a prototype-guided labeling method which minimizes redundant correlation computations for partial samples and assigns noisy coarse label groups efficiently. The coarse label groups will be continuously refined via label-flexible training network with an adaptive selection strategy. With the adapted domains and labels, FOUS can achieve the state-of-the-art (SOTA) performance on two benchmark datasets, CUHK-SYSU and PRW. The code is available at https://github.com/whbdmu/FOUS.

cs.CV↗

arXiv2023

Domain Adaptive Person Search via GAN-based Scene Synthesis for Cross-scene Videos

Person search has recently been a challenging task in the computer vision domain, which aims to search specific pedestrians from real cameras.Nevertheless, most surveillance videos comprise only a handful of images of each pedestrian, which often feature identical backgrounds and clothing. Hence, it is difficult to learn more discriminative features for person search in real scenes. To tackle this challenge, we draw on Generative Adversarial Networks (GAN) to synthesize data from surveillance videos. GAN has thrived in computer vision problems because it produces high-quality images efficiently. We merely alter the popular Fast R-CNN model, which is capable of processing videos and yielding accurate detection outcomes. In order to appropriately relieve the pressure brought by the two-stage model, we design an Assisted-Identity Query Module (AIDQ) to provide positive images for the behind part. Besides, the proposed novel GAN-based Scene Synthesis model that can synthesize high-quality cross-id person images for person search tasks. In order to facilitate the feature learning of the GAN-based Scene Synthesis model, we adopt an online learning strategy that collaboratively learns the synthesized images and original images. Extensive experiments on two widely used person search benchmarks, CUHK-SYSU and PRW, have shown that our method has achieved great performance, and the extensive ablation study further justifies our GAN-synthetic data can effectively increase the variability of the datasets and be more realistic.

cs.CV↗

arXiv2021

Analytics and Machine Learning in Vehicle Routing Research

The Vehicle Routing Problem (VRP) is one of the most intensively studied combinatorial optimisation problems for which numerous models and algorithms have been proposed. To tackle the complexities, uncertainties and dynamics involved in real-world VRP applications, Machine Learning (ML) methods have been used in combination with analytical approaches to enhance problem formulations and algorithmic performance across different problem solving scenarios. However, the relevant papers are scattered in several traditional research fields with very different, sometimes confusing, terminologies. This paper presents a first, comprehensive review of hybrid methods that combine analytical techniques with ML tools in addressing VRP problems. Specifically, we review the emerging research streams on ML-assisted VRP modelling and ML-assisted VRP optimisation. We conclude that ML can be beneficial in enhancing VRP modelling, and improving the performance of algorithms for both online and offline VRP optimisations. Finally, challenges and future opportunities of VRP research are discussed.

cs.LG↗