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Suwei Zhang

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JBHI Journal 2025 Journal Article

SFM-Net: Semantic Feature-Based Multi-Stage Network for Unsupervised Image Registration

  • Tai Ma
  • Xinru Dai
  • Suwei Zhang
  • Haidong Zou
  • Lianghua He
  • Ying Wen

It is difficult for general registration methods to establish the fine correspondence between images with complex anatomical structures. To overcome the above problem, this work presents SFM-Net, an unsupervised multi-stage semantic feature-based network. In addition to using the pixel-based similarity metrics, we propose a feature operator and emphasize a feature registration to improve the alignment of semantic related areas. Specifically, we design a two-stage training strategy, the intensity image registration stage and the semantic feature registration stage. The former is for valid semantic features learning and intensity-based coarse registration, while the latter is for semantic areas alignment, achieving fine transformation of anatomical structure. The same structure of both stages is composed of a dual-stream feature extraction module (DFEM) and a refined deformation field generation module (RDGM). Unlike the deep learning-based approaches that utilizing down-sampled encoder to extract features, DFEM constructed by dual-stream U-Net structure can capture semantic information in decoder feature for structural alignment. Different with approaches applying cascaded networks to learn deformation field, our proposed RDGM generates multi-scale deformation fields by performing a coarse-to-fine registration within a single network. Experiments on 3D brain MRI and liver CT datasets confirm that the proposed SFM-Net achieves accurate and diffeomorphic registration results, outperforming other state-of-the-art methods.

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AAAI Conference 2019 Conference Paper

An Integral Tag Recommendation Model for Textual Content

  • Shijie Tang
  • Yuan Yao
  • Suwei Zhang
  • Feng Xu
  • Tianxiao Gu
  • Hanghang Tong
  • Xiaohui Yan
  • Jian Lu

Recommending suitable tags for online textual content is a key building block for better content organization and consumption. In this paper, we identify three pillars that impact the accuracy of tag recommendation: (1) sequential text modeling meaning that the intrinsic sequential ordering as well as different areas of text might have an important implication on the corresponding tag(s), (2) tag correlation meaning that the tags for a certain piece of textual content are often semantically correlated with each other, and (3) content-tag overlapping meaning that the vocabularies of content and tags are overlapped. However, none of the existing methods consider all these three aspects, leading to a suboptimal tag recommendation. In this paper, we propose an integral model to encode all the three aspects in a coherent encoder-decoder framework. In particular, (1) the encoder models the semantics of the textual content via Recurrent Neural Networks with the attention mechanism, (2) the decoder tackles the tag correlation with a prediction path, and (3) a shared embedding layer and an indicator function across encoder-decoder address the content-tag overlapping. Experimental results on three realworld datasets demonstrate that the proposed method significantly outperforms the existing methods in terms of recommendation accuracy.

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AAAI Conference 2019 Conference Paper

Hashtag Recommendation for Photo Sharing Services

  • Suwei Zhang
  • Yuan Yao
  • Feng Xu
  • Hanghang Tong
  • Xiaohui Yan
  • Jian Lu

Hashtags can greatly facilitate content navigation and improve user engagement in social media. Meaningful as it might be, recommending hashtags for photo sharing services such as Instagram and Pinterest remains a daunting task due to the following two reasons. On the endogenous side, posts in photo sharing services often contain both images and text, which are likely to be correlated with each other. Therefore, it is crucial to coherently model both image and text as well as the interaction between them. On the exogenous side, hashtags are generated by users and different users might come up with different tags for similar posts, due to their different preference and/or community effect. Therefore, it is highly desirable to characterize the users’ tagging habits. In this paper, we propose an integral and effective hashtag recommendation approach for photo sharing services. In particular, the proposed approach considers both the endogenous and exogenous effects by a content modeling module and a habit modeling module, respectively. For the content modeling module, we adopt the parallel co-attention mechanism to coherently model both image and text as well as the interaction between them; for the habit modeling module, we introduce an external memory unit to characterize the historical tagging habit of each user. The overall hashtag recommendations are generated on the basis of both the post features from the content modeling module and the habit influences from the habit modeling module. We evaluate the proposed approach on real Instagram data. The experimental results demonstrate that the proposed approach significantly outperforms the state-of-theart methods in terms of recommendation accuracy, and that both content modeling and habit modeling contribute significantly to the overall recommendation accuracy.

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