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Minghui Wu

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4 papers

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JBHI Journal 2023 Journal Article

Chromosome Detection in Metaphase Cell Images Using Morphological Priors

Jun Wang
Chengfeng Zhou
Songchang Chen
Jianwu Hu
Minghui Wu
Xudong Jiang
Chenming Xu
Dahong Qian

Reliable chromosome detection in metaphase cell (MC) images can greatly alleviate the workload of cytogeneticists for karyotype analysis and the diagnosis of chromosomal disorders. However, it is still an extremely challenging task due to the complicated characteristics of chromosomes, e. g. , dense distributions, arbitrary orientations, and various morphologies. In this article, we propose a novel rotated-anchor-based detection framework, named DeepCHM, for fast and accurate chromosome detection in MC images. Our framework has three main innovations: 1) A deep saliency map representing chromosomal morphological features is learned end-to-end with semantic features. This not only enhances the feature representations for anchor classification and regression but also guides the anchor setting to significantly reduce redundant anchors. This accelerates the detection and improves the performance; 2) A hardness-aware loss weights the contribution of positive anchors, which effectively reinforces the model to identify hard chromosomes; 3) A model-driven sampling strategy addresses the anchor imbalance issue by adaptively selecting hard negative anchors for model training. In addition, a large-scale benchmark dataset with a total of 624 images and 27, 763 chromosome instances was built for chromosome detection and segmentation. Extensive experimental results demonstrate that our method outperforms most state-of-the-art (SOTA) approaches and successfully handles chromosome detection, with an AP score of 93. 53%.

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AAMAS Conference 2023 Conference Paper

Efficient Interactive Recommendation via Huffman Tree-based Policy Learning

Longxiang Shi
Zilin Zhang
Shoujin Wang
Binbin Zhou
Minghui Wu
Cheng Yang
Shijian Li

Interactive recommender systems (IRSs) are an essential part of our daily life, as they can suggest items to persistently satisfy our demands. Due to the interactive nature, conventional static recommendation methods such as matrix factorization, and content-based filtering are ineffective to capture the dynamic preferences of users. Recently, reinforcement learning (RL) has shown great potential in addressing the challenges in IRSs, since it can capture users’ dynamic preferences and model the long-term profit of user-item interactions. However, millions of items in real-world IRSs lead to a large discrete action space in the RL setting, rendering RL-based IRSs inefficient and hindering their widespread application. Such an inefficiency issue has not been well addressed in the literature. In order to address this issue, we propose a novel Huffman Tree Policy Recommendation (HTPR) framework. Specifically, a novel policy learning network based on a newly designed Huffman tree is proposed for policy representation learning, which effectively improves the learning efficiency. Moreover, a novel parameter-sharing scheme is devised to further reduce unnecessary computations. Extensive experiments on two real-world benchmark datasets demonstrate the superiority of HTPR over the state-of-the-art IRS methods in terms of both recommendation accuracy and efficiency.

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AAAI Conference 2018 Conference Paper

Distance-Aware DAG Embedding for Proximity Search on Heterogeneous Graphs

Zemin Liu
Vincent Zheng
Zhou Zhao
Fanwei Zhu
Kevin Chang
Minghui Wu
Jing Ying

Proximity search on heterogeneous graphs aims to measure the proximity between two nodes on a graph w. r. t. some semantic relation for ranking. Pioneer work often tries to measure such proximity by paths connecting the two nodes. However, paths as linear sequences have limited expressiveness for the complex network connections. In this paper, we explore a more expressive DAG (directed acyclic graph) data structure for modeling the connections between two nodes. Particularly, we are interested in learning a representation for the DAGs to encode the proximity between two nodes. We face two challenges to use DAGs, including how to ef- ﬁciently generate DAGs and how to effectively learn DAG embedding for proximity search. We ﬁnd distance-awareness as important for proximity search and the key to solve the above challenges. Thus we develop a novel Distance-aware DAG Embedding (D2AGE) model. We evaluate D2AGE on three benchmark data sets with six semantic relations, and we show that D2AGE outperforms the state-of-the-art baselines. We release the code on https: //github. com/shuaiOKshuai.

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AAAI Conference 2017 Conference Paper

Semantic Proximity Search on Heterogeneous Graph by Proximity Embedding

Zemin Liu
Vincent W. Zheng
Zhou Zhao
Fanwei Zhu
Kevin Chen-Chuan Chang
Minghui Wu
Jing Ying

Many real-world networks have a rich collection of objects. The semantics of these objects allows us to capture different classes of proximities, thus enabling an important task of semantic proximity search. As the core of semantic proximity search, we have to measure the proximity on a heterogeneous graph, whose nodes are various types of objects. Most of the existing methods rely on engineering features about the graph structure between two nodes to measure their proximity. With recent development on graph embedding, we see a good chance to avoid feature engineering for semantic proximity search. There is very little work on using graph embedding for semantic proximity search. We also observe that graph embedding methods typically focus on embedding nodes, which is an “indirect” approach to learn the proximity. Thus, we introduce a new concept of proximity embedding, which directly embeds the network structure between two possibly distant nodes. We also design our proximity embedding, so as to ﬂexibly support both symmetric and asymmetric proximities. Based on the proximity embedding, we can easily estimate the proximity score between two nodes and enable search on the graph. We evaluate our proximity embedding method on three real-world public data sets, and show it outperforms the state-of-the-art baselines. We release the code for proximity embedding1.

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