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Qiang Ma

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3 papers

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JBHI Journal 2026 Journal Article

DEW-Net: A W-Shaped Dual-Encoder Network with Attention Fusion Mechanisms for Pathological H&E Image Segmentation

Fuhan Meng
Xixiang Deng
Yingbo Qu
Chentao Li
Qiang Ma
Yusong Mao
Xinwei Zhang
Pan Huang

Segmenting programmed cell death-ligand 1 (PD-L1) expression regions in lung squamous cell carcinoma from pathological H&E images represents a challenging pixel-level prediction task, attributed to the morphological heterogeneity and size discrepancies of expression areas. Although hybrid architectures of CNN and Transformer can extract local features and capture long-range dependencies, they inadequately address information interaction and redundant information elimination during the fusion process, adversely impacting PD-L1 segmentation accuracy. To address this, we propose a W-shaped dual-encoder network (DEW-Net) with novel attention fusion mechanisms. First, a CNN encoder and a Swin Transformer encoder are connected in parallel to extract multi-layer local and global features from pathological images, respectively. Second, a Cross-Attention Fusion (CAF) module is proposed to strengthen information interaction and semantic feature fusion. Additionally, a Channel Attention (CA) is introduced in skip connections to enhance the channel-wise information of shallow features, while a Bilateral-voting Position Attention (BPA) module is further proposed to eliminate positional noise in same-scale shallow features and reinforce position-wise information. We conducted extensive experiments on four datasets. On the PD-L1 segmentation dataset, DEW-Net achieved superior performance, with DSC and IoU reaching 79. 93% and 71. 27%, respectively. These results demonstrate its strong performance and generalization capability compared to other state-of-the-art (SOTA) methods.

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AAAI Conference 2026 Conference Paper

Prototype-Driven Active Domain Adaptation with Density Consideration

Zeyu Zhang
Chun Shen
Qiang Ma
Meng Kang
Shuai Lü

Active domain adaptation (ADA) aims to select a small set of target samples for annotation and use them for training to maximally boost the adaptation performance. However, most existing ADA methods only rely on the original output of the model, without considering the relationship between the source and target domain features, which may lead to selecting uninformative samples. In this paper, we propose an effective ADA framework: Prototype-Driven Active Domain Adaptation with density consideration (PDADA). It selects the most valuable target samples in the presence of domain shift through two criteria: Density-Conscious Domainness (DCD) and Prototype-Driven Informativeness (PDI). Furthermore, considering the class imbalance and cluster looseness issues in sample selection and domain adaptation, we develop a Class Balanced Expansion (CBE) algorithm and the Adversarial Active Domain Adaptation via Protecting Structured Information (AADA-PSI). Extensive experiments demonstrate that under the cooperation of the above components, PDADA outperforms previous methods on several challenging benchmarks and can be generalized to multi-source active domain adaptation setting.

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IJCAI Conference 2017 Conference Paper

Understanding People Lifestyles: Construction of Urban Movement Knowledge Graph from GPS Trajectory

Chenyi Zhuang
Nicholas Jing Yuan
Ruihua Song
Xing Xie
Qiang Ma

Technologies are increasingly taking advantage of the explosion in the amount of data generated by social multimedia (e. g. , web searches, ad targeting, and urban computing). In this paper, we propose a multi-view learning framework for presenting the construction of a new urban movement knowledge graph, which could greatly facilitate the research domains mentioned above. In particular, by viewing GPS trajectory data from temporal, spatial, and spatiotemporal points of view, we construct a knowledge graph of which nodes and edges are their locations and relations, respectively. On the knowledge graph, both nodes and edges are represented in latent semantic space. We verify its utility by subsequently applying the knowledge graph to predict the extent of user attention (high or low) paid to different locations in a city. Experimental evaluations and analysis of a real-world dataset show significant improvements in comparison to state-of-the-art methods.

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