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Xiangjian He

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5 papers

1 author row

JBHI Journal 2026 Journal Article

Accurate Segmentation of Surgical Instruments via Spectral-Attentive Contextual Interaction Network

Jiaxin Mei
Yizhe Zhang
Xiangjian He
Tao Zhou

Surgical instrument segmentation is crucial for enhancing visual perception and enabling precise manipulation in robotic surgical systems. However, current segmentation models continue to face substantial challenges in terms of accuracy and robustness due to complex background interference, diverse instrument morphologies, and low contrast between instruments and surrounding tissues in surgical environments. Despite significant advances in deep learning-based approaches, existing models still fall short in capturing the fine edges and global contextual relationships of instruments. To address these issues, we propose a Spectral-attentive Contextual Interaction Network (SCI-Net) for surgical instrument segmentation. Specifically, we present a Global Context Aggregation Module (GCAM) to integrate high-level features, which is used to produce a global map for the coarse localization of the segmented target. Then, a Spectral-enhanced Feature Module (SFM) is proposed to enhance the expression of features in the form of frequency-domain attention by transforming features from the spatial domain to the frequency domain. In addition, we design the Scale-aware Dilation Module (SDM) in the decoder to further adaptively integrate the augmented features through multi-scale dilation convolution combined with a dynamic fusion mechanism, which improves the segmentation performance on the fine boundaries of instruments. We have extensively validated SCI-Net on multiple publicly available surgical instrument segmentation datasets, and the experimental results show that SCI-Net significantly outperforms other state-of-the-art segmentation methods.

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AIIM Journal 2024 Journal Article

Automatic quantitative stroke severity assessment based on Chinese clinical named entity recognition with domain-adaptive pre-trained large language model

Zhanzhong Gu
Xiangjian He
Ping Yu
Wenjing Jia
Xiguang Yang
Gang Peng
Penghui Hu
Shiyan Chen

Background: Stroke is a prevalent disease with a significant global impact. Effective assessment of stroke severity is vital for an accurate diagnosis, appropriate treatment, and optimal clinical outcomes. The National Institutes of Health Stroke Scale (NIHSS) is a widely used scale for quantitatively assessing stroke severity. However, the current manual scoring of NIHSS is labor-intensive, time-consuming, and sometimes unreliable. Applying artificial intelligence (AI) techniques to automate the quantitative assessment of stroke on vast amounts of electronic health records (EHRs) has attracted much interest. Objective: This study aims to develop an automatic, quantitative stroke severity assessment framework through automating the entire NIHSS scoring process on Chinese clinical EHRs. Methods: Our approach consists of two major parts: Chinese clinical named entity recognition (CNER) with a domain-adaptive pre-trained large language model (LLM) and automated NIHSS scoring. To build a high-performing CNER model, we first construct a stroke-specific, densely annotated dataset “Chinese Stroke Clinical Records” (CSCR) from EHRs provided by our partner hospital, based on a stroke ontology that defines semantically related entities for stroke assessment. We then pre-train a Chinese clinical LLM coined “CliRoberta” through domain-adaptive transfer learning and construct a deep learning-based CNER model that can accurately extract entities directly from Chinese EHRs. Finally, an automated, end-to-end NIHSS scoring pipeline is proposed by mapping the extracted entities to relevant NIHSS items and values, to quantitatively assess the stroke severity. Results: Results obtained on a benchmark dataset CCKS2019 and our newly created CSCR dataset demonstrate the superior performance of our domain-adaptive pre-trained LLM and the CNER model, compared with the existing benchmark LLMs and CNER models. The high F1 score of 0. 990 ensures the reliability of our model in accurately extracting the entities for the subsequent automatic NIHSS scoring. Subsequently, our automated, end-to-end NIHSS scoring approach achieved excellent inter-rater agreement (0. 823) and intraclass consistency (0. 986) with the ground truth and significantly reduced the processing time from minutes to a few seconds. Conclusion: Our proposed automatic and quantitative framework for assessing stroke severity demonstrates exceptional performance and reliability through directly scoring the NIHSS from diagnostic notes in Chinese clinical EHRs. Moreover, this study also contributes a new clinical dataset, a pre-trained clinical LLM, and an effective deep learning-based CNER model. The deployment of these advanced algorithms can improve the accuracy and efficiency of clinical assessment, and help improve the quality, affordability and productivity of healthcare services.

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AAAI Conference 2024 Conference Paper

Scale Optimization Using Evolutionary Reinforcement Learning for Object Detection on Drone Imagery

Jialu Zhang
Xiaoying Yang
Wentao He
Jianfeng Ren
Qian Zhang
Yitian Zhao
Ruibin Bai
Xiangjian He

Object detection in aerial imagery presents a significant challenge due to large scale variations among objects. This paper proposes an evolutionary reinforcement learning agent, integrated within a coarse-to-fine object detection framework, to optimize the scale for more effective detection of objects in such images. Specifically, a set of patches potentially containing objects are first generated. A set of rewards measuring the localization accuracy, the accuracy of predicted labels, and the scale consistency among nearby patches are designed in the agent to guide the scale optimization. The proposed scale-consistency reward ensures similar scales for neighboring objects of the same category. Furthermore, a spatial-semantic attention mechanism is designed to exploit the spatial semantic relations between patches. The agent employs the proximal policy optimization strategy in conjunction with the evolutionary strategy, effectively utilizing both the current patch status and historical experience embedded in the agent. The proposed model is compared with state-of-the-art methods on two benchmark datasets for object detection on drone imagery. It significantly outperforms all the compared methods. Code is available at https://github.com/UNNC-CV/EvOD/.

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AAAI Conference 2022 Conference Paper

Channelized Axial Attention – considering Channel Relation within Spatial Attention for Semantic Segmentation

Ye Huang
Di Kang
Wenjing Jia
Liu Liu
Xiangjian He

Spatial and channel attentions, modelling the semantic interdependencies in spatial and channel dimensions respectively, have recently been widely used for semantic segmentation. However, computing spatial and channel attentions separately sometimes causes errors, especially for those difficult cases. In this paper, we propose Channelized Axial Attention (CAA) to seamlessly integrate channel attention and spatial attention into a single operation with negligible computation overhead. Specifically, we break down the dot-product operation of the spatial attention into two parts and insert channel relation in between, allowing for independently optimized channel attention on each spatial location. We further develop grouped vectorization, which allows our model to run with very little memory consumption without slowing down the running speed. Comparative experiments conducted on multiple benchmark datasets, including Cityscapes, PAS- CAL Context, and COCO-Stuff, demonstrate that our CAA outperforms many state-of-the-art segmentation models (including dual attention) on all tested datasets.

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IJCAI Conference 2016 Conference Paper

A Generative Model for Recognizing Mixed Group Activities in Still Images

Zheng Zhou
Kan Li
Xiangjian He
Mengmeng Li

Recognizing multiple mixed group activities from one still image is not a hard problem for humans but remains highly challenging for computer recognition systems. When modeling interactions among multiple units (i. e. , more than two groups or persons), the existing approaches tend to divide them into interactions between pairwise units. However, no mathematical evidence supports this transformation. Therefore, these approaches' performance is limited on images containing multiple activities. In this paper, we propose a generative model to provide a more reasonable interpretation for the mixed group activities contained in one image. We design a four level structure and convert the original intra-level interactions into inter-level interactions, in order to implement both interactions among multiple groups and interactions among multiple persons within a group. The proposed four-level structure makes our model more robust against the occlusion and overlap of the visible poses in images. Experimental results demonstrate that our model makes good interpretations for mixed group activities and outperforms the state-of-the-art methods on the Collective Activity Classification dataset.

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