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Article ; Online: Temporal-Spatial Correlation Attention Network for Clinical Data Analysis in Intensive Care Unit.

Nie, Weizhi / Yu, Yuhe / Zhang, Chen / Song, Dan / Zhao, Lina / Bai, Yunpeng

IEEE transactions on bio-medical engineering

2024 Volume 71, Issue 2, Page(s) 583–595

Abstract: Recent advancements in medical information technology have enabled electronic health records (EHRs) to store comprehensive clinical data which has ushered healthcare into the era of "big data". However, medical data are rather complicated, making problem- ...

Abstract	Recent advancements in medical information technology have enabled electronic health records (EHRs) to store comprehensive clinical data which has ushered healthcare into the era of "big data". However, medical data are rather complicated, making problem-solving in healthcare be limited in scope and comprehensiveness. The rapid development of deep learning in recent years has opened up opportunities for leveraging big data in healthcare. In this article we introduce a temporal-spatial correlation attention network (TSCAN) to address various clinical characteristic prediction problems, including mortality prediction, length of stay prediction, physiologic decline detection, and phenotype classification. Leveraging the attention mechanism model's design, our approach efficiently identifies relevant items in clinical data and temporally correlated nodes based on specific tasks, resulting in improved prediction accuracy. Additionally, our method identifies crucial clinical indicators associated with significant outcomes, which can inform and enhance treatment options. Our experiments utilize data from the publicly accessible Medical Information Mart for Intensive Care (MIMIC-IV) database. Finally, our approach demonstrates notable performance improvements of 2.0% (metric) compared to other SOTA prediction methods. Specifically, we achieved an impressive 90.7% mortality rate prediction accuracy and 45.1% accuracy in length of stay prediction.
MeSH term(s)	Humans ; Intensive Care Units ; Critical Care ; Electronic Health Records ; Databases, Factual ; Medical Informatics
Language	English
Publishing date	2024-01-19
Publishing country	United States
Document type	Journal Article
ZDB-ID	160429-6
ISSN	1558-2531 ; 0018-9294
ISSN (online)	1558-2531
ISSN	0018-9294
DOI	10.1109/TBME.2023.3309956
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: HMNet: Hierarchical Multi-Scale Brain Tumor Segmentation Network.

Zhang, Ruifeng / Jia, Shasha / Adamu, Mohammed Jajere / Nie, Weizhi / Li, Qiang / Wu, Ting

Journal of clinical medicine

2023 Volume 12, Issue 2

Abstract: An accurate and efficient automatic brain tumor segmentation algorithm is important for clinical practice. In recent years, there has been much interest in automatic segmentation algorithms that use convolutional neural networks. In this paper, we ... ...

Abstract	An accurate and efficient automatic brain tumor segmentation algorithm is important for clinical practice. In recent years, there has been much interest in automatic segmentation algorithms that use convolutional neural networks. In this paper, we propose a novel hierarchical multi-scale segmentation network (HMNet), which contains a high-resolution branch and parallel multi-resolution branches. The high-resolution branch can keep track of the brain tumor's spatial details, and the multi-resolution feature exchange and fusion allow the network's receptive fields to adapt to brain tumors of different shapes and sizes. In particular, to overcome the large computational overhead caused by expensive 3D convolution, we propose a lightweight conditional channel weighting block to reduce GPU memory and improve the efficiency of HMNet. We also propose a lightweight multi-resolution feature fusion (LMRF) module to further reduce model complexity and reduce the redundancy of the feature maps. We run tests on the BraTS 2020 dataset to determine how well the proposed network would work. The dice similarity coefficients of HMNet for ET, WT, and TC are 0.781, 0.901, and 0.823, respectively. Many comparative experiments on the BraTS 2020 dataset and other two datasets show that our proposed HMNet has achieved satisfactory performance compared with the SOTA approaches.
Language	English
Publishing date	2023-01-09
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2662592-1
ISSN	2077-0383
ISSN	2077-0383
DOI	10.3390/jcm12020538
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Article ; Online: Deep Correlated Joint Network for 2-D Image-Based 3-D Model Retrieval.

Nie, Wei-Zhi / Liu, An-An / Zhao, Sicheng / Gao, Yue

IEEE transactions on cybernetics

2022 Volume 52, Issue 3, Page(s) 1862–1871

Abstract: In this article, we propose a novel deep correlated joint network (DCJN) approach for 2-D image-based 3-D model retrieval. First, the proposed method can jointly learn two distinct deep neural networks, which are trained for individual modalities to ... ...

Abstract	In this article, we propose a novel deep correlated joint network (DCJN) approach for 2-D image-based 3-D model retrieval. First, the proposed method can jointly learn two distinct deep neural networks, which are trained for individual modalities to learn two deep nonlinear transformations for visual feature extraction from the co-embedding feature space. Second, we propose the global loss function for the DCJN, consisting of a discriminative loss and a correlation loss. The discriminative loss aims to minimize the intraclass distance of the extracted features and maximize the interclass distance of such features to a large margin within each modality, while the correlation loss focuses on mitigating the distribution discrepancy across different modalities. Consequently, the proposed method can realize cross-modality feature extraction guided by the defined global loss function to benefit the similarity measure between 2-D images and 3-D models. For a comparison experiment, we contribute the current largest 2-D image-based 3-D model retrieval dataset. Moreover, the proposed method was further evaluated on three popular benchmarks, including the 3-D Shape Retrieval Contest 2014, 2016, and 2018 benchmarks. The extensive comparison experimental results demonstrate the superiority of this method over the state-of-the-art methods.
MeSH term(s)	Algorithms ; Deep Learning ; Diagnostic Imaging ; Humans ; Models, Biological
Language	English
Publishing date	2022-03-11
Publishing country	United States
Document type	Journal Article
ISSN	2168-2275
ISSN (online)	2168-2275
DOI	10.1109/TCYB.2020.2995415
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: [Retrospective study on the modified Uhl technique of closed reduction and percutaneous pin in the treatment of Colles' fracture].

Li, Zhao-Hui / Sun, Zhong-Yi / Nie, Zhen / Chen, Yu / Nie, Wei-Zhi

Zhongguo gu shang = China journal of orthopaedics and traumatology

2023 Volume 36, Issue 9, Page(s) 821–826

Abstract: Objective: To retrospectively assess the advantages of the modified Uhl technique in the treatment of Colles' fracture guided by the principles of Chinese osteosynthesis (CO) concept.: Methods: A retrospective study was conducted on 358 patients with ...

Abstract	Objective: To retrospectively assess the advantages of the modified Uhl technique in the treatment of Colles' fracture guided by the principles of Chinese osteosynthesis (CO) concept. Methods: A retrospective study was conducted on 358 patients with Colles' fracture treated with the modified Uhl technique of closed reduction and percutaneous pin between January 2016 and June 2021. Out of these, 120 eligible cases were selected and categorized into two groups according to different surgical methods:the closed reduction and percutaneous pin group, and the open reduction group. Sixty-eight patients in the closed reduction and percutaneous pin group were treated with the modified Uhl technique, while fifty-two patients in the open reduction group were treated with open reduction and internal fixation using plates. The modified Sarmiento imaging score, Gartland-Werley wrist score, operation time, hospital stay, and treatment costs between the two groups were compared at a 6-month postoperative follow-up. Results: There were no significant differences in terms of gender, age, affected side, injure factors, time of injury to surgery, Sarmiento imaging score, and Gartland-Werley wrist joint score ( Conclusion: The modified Uhl technique presents notable advantages in the management of Colles' fracture, including reliable fixation, less trauma, shorter operation time, less pain, shorter hospital stay, and cost-effectiveness. This technique exhibits promising potential for broader clinical application. However, it is important to note that the pin could potentially damage tendons, and in cases of Colles' fractures with osteoporosis and comminuted fragments, additional techniques may be required for reliable fixation.
MeSH term(s)	Humans ; Retrospective Studies ; Colles' Fracture/surgery ; Fracture Fixation, Internal ; Fractures, Comminuted ; Hospitalization
Language	Chinese
Publishing date	2023-09-21
Publishing country	China
Document type	English Abstract ; Journal Article
ISSN	1003-0034
ISSN	1003-0034
DOI	10.12200/j.issn.1003-0034.2023.09.006
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Deep reinforcement learning framework for thoracic diseases classification via prior knowledge guidance.

Nie, Weizhi / Zhang, Chen / Song, Dan / Zhao, Lina / Bai, Yunpeng / Xie, Keliang / Liu, Anan

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society

2023 Volume 108, Page(s) 102277

Abstract: The chest X-ray is commonly employed in the diagnosis of thoracic diseases. Over the years, numerous approaches have been proposed to address the issue of automatic diagnosis based on chest X-rays. However, the limited availability of labeled data for ... ...

Abstract	The chest X-ray is commonly employed in the diagnosis of thoracic diseases. Over the years, numerous approaches have been proposed to address the issue of automatic diagnosis based on chest X-rays. However, the limited availability of labeled data for related diseases remains a significant challenge in achieving accurate diagnoses. This paper focuses on the diagnostic problem of thorax diseases and presents a novel deep reinforcement learning framework. This framework incorporates prior knowledge to guide the learning process of diagnostic agents, and the model parameters can be continually updated as more data becomes available, mimicking a person's learning process. Specifically, our approach offers two key contributions: (1) prior knowledge can be acquired from pre-trained models using old data or similar data from other domains, effectively reducing the dependence on target domain data; and (2) the reinforcement learning framework enables the diagnostic agent to be as exploratory as a human, leading to improved diagnostic accuracy through continuous exploration. Moreover, this method effectively addresses the challenge of learning models with limited data, enhancing the model's generalization capability. We evaluate the performance of our approach using the well-known NIH ChestX-ray 14 and CheXpert datasets, and achieve competitive results. More importantly, in clinical application, we make considerable progress. The source code for our approach can be accessed at the following URL: https://github.com/NeaseZ/MARL.
MeSH term(s)	Humans ; Learning ; Thoracic Diseases/diagnostic imaging ; Thorax ; Software
Language	English
Publishing date	2023-07-29
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	639451-6
ISSN	1879-0771 ; 0895-6111
ISSN (online)	1879-0771
ISSN	0895-6111
DOI	10.1016/j.compmedimag.2023.102277
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: DAN: Deep-Attention Network for 3D Shape Recognition.

Nie, Weizhi / Zhao, Yue / Song, Dan / Gao, Yue

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society

2021 Volume 30, Page(s) 4371–4383

Abstract: Due to the wide applications in a rapidly increasing number of different fields, 3D shape recognition has become a hot topic in the computer vision field. Many approaches have been proposed in recent years. However, there remain huge challenges in two ... ...

Abstract	Due to the wide applications in a rapidly increasing number of different fields, 3D shape recognition has become a hot topic in the computer vision field. Many approaches have been proposed in recent years. However, there remain huge challenges in two aspects: exploring the effective representation of 3D shapes and reducing the redundant complexity of 3D shapes. In this paper, we propose a novel deep-attention network (DAN) for 3D shape representation based on multiview information. More specifically, we introduce the attention mechanism to construct a deep multiattention network that has advantages in two aspects: 1) information selection, in which DAN utilizes the self-attention mechanism to update the feature vector of each view, effectively reducing the redundant information, and 2) information fusion, in which DAN applies attention mechanism that can save more effective information by considering the correlations among views. Meanwhile, deep network structure can fully consider the correlations to continuously fuse effective information. To validate the effectiveness of our proposed method, we conduct experiments on the public 3D shape datasets: ModelNet40, ModelNet10, and ShapeNetCore55. Experimental results and comparison with state-of-the-art methods demonstrate the superiority of our proposed method. Code is released on https://github.com/RiDang/DANN.
Language	English
Publishing date	2021-04-21
Publishing country	United States
Document type	Journal Article
ISSN	1941-0042
ISSN (online)	1941-0042
DOI	10.1109/TIP.2021.3071687
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: T2TD

Nie, Weizhi / Chen, Ruidong / Wang, Weijie / Lepri, Bruno / Sebe, Nicu

Text-3D Generation Model based on Prior Knowledge Guidance

2023

Abstract: In recent years, 3D models have been utilized in many applications, such as auto-driver, 3D reconstruction, VR, and AR. However, the scarcity of 3D model data does not meet its practical demands. Thus, generating high-quality 3D models efficiently from ... ...

Abstract	In recent years, 3D models have been utilized in many applications, such as auto-driver, 3D reconstruction, VR, and AR. However, the scarcity of 3D model data does not meet its practical demands. Thus, generating high-quality 3D models efficiently from textual descriptions is a promising but challenging way to solve this problem. In this paper, inspired by the ability of human beings to complement visual information details from ambiguous descriptions based on their own experience, we propose a novel text-3D generation model (T2TD), which introduces the related shapes or textual information as the prior knowledge to improve the performance of the 3D generation model. In this process, we first introduce the text-3D knowledge graph to save the relationship between 3D models and textual semantic information, which can provide the related shapes to guide the target 3D model generation. Second, we integrate an effective causal inference model to select useful feature information from these related shapes, which removes the unrelated shape information and only maintains feature information that is strongly relevant to the textual description. Meanwhile, to effectively integrate multi-modal prior knowledge into textual information, we adopt a novel multi-layer transformer structure to progressively fuse related shape and textual information, which can effectively compensate for the lack of structural information in the text and enhance the final performance of the 3D generation model. The final experimental results demonstrate that our approach significantly improves 3D model generation quality and outperforms the SOTA methods on the text2shape datasets.
Keywords	Computer Science - Computer Vision and Pattern Recognition
Subject code	004
Publishing date	2023-05-25
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Point Cloud Completion Guided by Prior Knowledge via Causal Inference

Gao, Songxue / Jiao, Chuanqi / Chen, Ruidong / Wang, Weijie / Nie, Weizhi

2023

Abstract: Point cloud completion aims to recover raw point clouds captured by scanners from partial observations caused by occlusion and limited view angles. This makes it hard to recover details because the global feature is unlikely to capture the full details ... ...

Abstract	Point cloud completion aims to recover raw point clouds captured by scanners from partial observations caused by occlusion and limited view angles. This makes it hard to recover details because the global feature is unlikely to capture the full details of all missing parts. In this paper, we propose a novel approach to point cloud completion task called Point-PC, which uses a memory network to retrieve shape priors and designs a causal inference model to filter missing shape information as supplemental geometric information to aid point cloud completion. Specifically, we propose a memory operating mechanism where the complete shape features and the corresponding shapes are stored in the form of ``key-value'' pairs. To retrieve similar shapes from the partial input, we also apply a contrastive learning-based pre-training scheme to transfer the features of incomplete shapes into the domain of complete shape features. Experimental results on the ShapeNet-55, PCN, and KITTI datasets demonstrate that Point-PC outperforms the state-of-the-art methods.
Keywords	Computer Science - Computer Vision and Pattern Recognition
Subject code	004
Publishing date	2023-05-28
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: MV-CLIP

Song, Dan / Fu, Xinwei / Nie, Weizhi / Li, Wenhui / Liu, Anan

Multi-View CLIP for Zero-shot 3D Shape Recognition

2023

Abstract: Large-scale pre-trained models have demonstrated impressive performance in vision and language tasks within open-world scenarios. Due to the lack of comparable pre-trained models for 3D shapes, recent methods utilize language-image pre-training to ... ...

Abstract	Large-scale pre-trained models have demonstrated impressive performance in vision and language tasks within open-world scenarios. Due to the lack of comparable pre-trained models for 3D shapes, recent methods utilize language-image pre-training to realize zero-shot 3D shape recognition. However, due to the modality gap, pretrained language-image models are not confident enough in the generalization to 3D shape recognition. Consequently, this paper aims to improve the confidence with view selection and hierarchical prompts. Leveraging the CLIP model as an example, we employ view selection on the vision side by identifying views with high prediction confidence from multiple rendered views of a 3D shape. On the textual side, the strategy of hierarchical prompts is proposed for the first time. The first layer prompts several classification candidates with traditional class-level descriptions, while the second layer refines the prediction based on function-level descriptions or further distinctions between the candidates. Remarkably, without the need for additional training, our proposed method achieves impressive zero-shot 3D classification accuracies of 84.44\%, 91.51\%, and 66.17\% on ModelNet40, ModelNet10, and ShapeNet Core55, respectively. Furthermore, we will make the code publicly available to facilitate reproducibility and further research in this area.
Keywords	Computer Science - Computer Vision and Pattern Recognition
Subject code	004
Publishing date	2023-11-30
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: 3D Shape Knowledge Graph for Cross-domain 3D Shape Retrieval

Chang, Rihao / Ma, Yongtao / Hao, Tong / Nie, Weizhi

2022

Abstract: The surge in 3D modeling has led to a pronounced research emphasis on the field of 3D shape retrieval. Numerous contemporary approaches have been put forth to tackle this intricate challenge. Nevertheless, effectively addressing the intricacies of cross- ... ...

Abstract	The surge in 3D modeling has led to a pronounced research emphasis on the field of 3D shape retrieval. Numerous contemporary approaches have been put forth to tackle this intricate challenge. Nevertheless, effectively addressing the intricacies of cross-modal 3D shape retrieval remains a formidable undertaking, owing to inherent modality-based disparities. This study presents an innovative notion, termed "geometric words", which functions as elemental constituents for representing entities through combinations. To establish the knowledge graph, we employ geometric words as nodes, connecting them via shape categories and geometry attributes. Subsequently, we devise a unique graph embedding method for knowledge acquisition. Finally, an effective similarity measure is introduced for retrieval purposes. Importantly, each 3D or 2D entity can anchor its geometric terms within the knowledge graph, thereby serving as a link between cross-domain data. As a result, our approach facilitates multiple cross-domain 3D shape retrieval tasks. We evaluate the proposed method's performance on the ModelNet40 and ShapeNetCore55 datasets, encompassing scenarios related to 3D shape retrieval and cross-domain retrieval. Furthermore, we employ the established cross-modal dataset (MI3DOR) to assess cross-modal 3D shape retrieval. The resulting experimental outcomes, in conjunction with comparisons against state-of-the-art techniques, clearly highlight the superiority of our approach.
Keywords	Computer Science - Computer Vision and Pattern Recognition
Subject code	004 ; 006
Publishing date	2022-10-26
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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