Article
Computer Science, Interdisciplinary Applications
Xu Chen, Chunfeng Lian, Li Wang, Hannah Deng, Tianshu Kuang, Steve Fung, Jaime Gateno, Pew-Thian Yap, James J. Xia, Dinggang Shen
Summary: An anatomy-regularized representation learning approach is proposed for segmentation-oriented cross-modality image synthesis, showing superiority in comparison with state-of-the-art cross-modality medical image segmentation methods.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2021)
Review
Computer Science, Information Systems
Irena Galic, Marija Habijan, Hrvoje Leventic, Kresimir Romic
Summary: This work provides an overview of fundamental concepts, state-of-the-art models, and publicly available datasets in the field of medical imaging, with a focus on the application of deep learning methods. It also discusses current research conducted in various medical imaging areas, challenges faced, and future research directions.
Article
Computer Science, Artificial Intelligence
Lihao Liu, Angelica I. Aviles-Rivero, Carola-Bibiane Schonlieb
Summary: Medical image segmentation is a crucial task in medical imaging, and this paper presents a novel unsupervised segmentation technique called CLMorph. By combining registration and contrastive learning, the proposed technique achieves improved accuracy in image segmentation.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Interdisciplinary Applications
Zejian Chen, Wei Zhuo, Tianfu Wang, Jun Cheng, Wufeng Xue, Dong Ni
Summary: In this study, a semi-supervised representation learning method is proposed to enhance the features in the encoder and decoder. The method outperforms existing methods in the segmentation of medical volumes and sequences, and achieves significant improvement even with few labeled data.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2023)
Article
Biology
Chae Eun Lee, Hyelim Park, Yeong-Gil Shin, Minyoung Chung
Summary: The research introduces an adversarial learning-based semi-supervised medical image segmentation method that effectively embeds local and global features and learns context relations between multiple classes. Experimental results show that the method performs well in both single-class and multi-class segmentation, successfully leveraging unlabeled data to improve network performance.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Genetics & Heredity
Yanping Li, Nian Fang, Haiquan Wang, Rui Wang
Summary: In this paper, a multi-modal medical image fusion algorithm based on geometric algebra sparse representation is proposed. The algorithm avoids the loss of correlation between channels and outperforms existing methods in subjective and objective quality evaluation.
FRONTIERS IN GENETICS
(2022)
Article
Computer Science, Artificial Intelligence
Krishna Chaitanya, Neerav Karani, Christian F. Baumgartner, Ertunc Erdil, Anton Becker, Olivio Donati, Ender Konukoglu
Summary: Supervised learning-based segmentation methods typically require a large number of annotated training data, which is challenging in medical applications. This work presents a novel task-driven data augmentation method that significantly outperforms other approaches in limited annotation settings.
MEDICAL IMAGE ANALYSIS
(2021)
Article
Computer Science, Artificial Intelligence
Arash Abdi, Mohammad Rahmati, Mohammad M. Ebadzadeh
Summary: In this paper, a new discriminative dictionary learning algorithm is proposed, which embeds an entropy-based criterion in the objective function to enforce a proper structure for dictionary items. Experimental results demonstrate that the algorithm outperforms other methods on various real-world image datasets.
PATTERN RECOGNITION
(2021)
Review
Dentistry, Oral Surgery & Medicine
Lang Zhang, Wang Li, Jinxun Lv, Jiajie Xu, Hengyu Zhou, Gen Li, Keqi Ai
Summary: This article reviews recent advances in computer-aided segmentation methods for oral and maxillofacial surgery and discusses their advantages and limitations. The study found that these methods can be divided into traditional image processing and machine learning categories, with machine learning methods showing unprecedented performance. However, challenges such as scarcity of datasets and visible artifacts in images still exist. Accurate image segmentation is crucial for precise treatment and surgical planning in oral and maxillofacial surgery.
JOURNAL OF DENTISTRY
(2023)
Article
Computer Science, Information Systems
Suit Mun Ng, Haniza Yazid, Nazahah Mustafa
Summary: This paper compares the quality of images produced by combining dictionary learning and sparse representation algorithms with those produced by using only sparse regularization methods. It explores the impact of implementing a dictionary learning process on enhancing low-resolution images.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Computer Science, Interdisciplinary Applications
Cheng Ouyang, Carlo Biffi, Chen Chen, Turkay Kart, Huaqi Qiu, Daniel Rueckert
Summary: Researchers proposed a self-supervised few-shot semantic segmentation framework for medical images, which bypasses the need for annotations during training by utilizing superpixel-based pseudo-labels and an adaptive local prototype pooling module to improve segmentation accuracy.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2022)
Article
Computer Science, Artificial Intelligence
Bao-Qing Yang, Xin-Ping Guan, Jun-Wu Zhu, Chao-Chen Gu, Kai-Jie Wu, Jia-Jie Xu
Summary: The paper presents a discriminative dictionary learning framework based on support vector machines and feedback mechanism to enhance image classification performance.
PATTERN RECOGNITION
(2021)
Article
Engineering, Biomedical
Hao Guan, Mingxia Liu
Summary: This paper surveys the recent advances of domain adaptation methods in medical image analysis, presents the motivation of introducing domain adaptation techniques to tackle domain heterogeneity issues in medical image analysis, and reviews the recent domain adaptation models in various medical image analysis tasks.
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
(2022)
Article
Computer Science, Interdisciplinary Applications
Chenyu You, Yuan Zhou, Ruihan Zhao, Lawrence Staib, James S. Duncan
Summary: Automated segmentation in medical image analysis is a challenging task. This work introduces a simple contrastive distillation framework, SimCVD, that improves the accuracy of image segmentation through unsupervised training and structural distillation. Experimental results on two datasets demonstrate the effectiveness of SimCVD.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2022)
Article
Public, Environmental & Occupational Health
Mengfang Li, Yuanyuan Jiang, Yanzhou Zhang, Haisheng Zhu
Summary: This article emphasizes the importance and advantages of using deep learning techniques in medical image analysis. It categorizes and evaluates various deep learning methods, finding that Python is the most commonly used programming language, and the majority of the reviewed papers were published recently, focusing on image analysis in medical healthcare domains. The article highlights the latest advancements and practical applications of DL techniques, while addressing the challenges hindering their widespread implementation.
FRONTIERS IN PUBLIC HEALTH
(2023)
Article
Public, Environmental & Occupational Health
Tracy Shicun Cui, Benjamin Lane, Yumeng Wu, Jing Ma, Rong Fu, Jianhua Hou, Siyan Meng, Lu Xie, Yuzhou Gu, Xiaojie Huang, Huang Zheng, Yanling Ma, Na He, Kathrine Meyers
Summary: This study examines the willingness to use PrEP for HIV prevention among GBMSM in China using structural equation modeling. The findings indicate that knowledge, attitudes, and subjective norms are significantly related to intention to use PrEP, while general beliefs about medicines have no significant impact. These findings suggest the importance of interventions targeting knowledge, positive attitudes, and social norms to promote PrEP uptake among sexually active GBMSM.
Article
Cardiac & Cardiovascular Systems
Taylor Powell, Saumik Rahman, Lawrence Staib, Shivank Bhatia, Raj Ayyagari
Summary: The study aimed to determine the inflection points of the operator learning curve for prostatic artery embolization (PAE) and their impact on technical efficiency, clinical outcomes, and adverse events. The results showed that operator technical efficiency plateaued after 73-78 PAE procedures, with substantial clinical improvements and low adverse event frequency/severity.
CARDIOVASCULAR AND INTERVENTIONAL RADIOLOGY
(2023)
Article
Engineering, Electrical & Electronic
Jichu Ou, Wanyi Li, Jingmin Huang, Xiaojie Huang, Xuan Xie
Summary: In this paper, the authors propose a novel neural network, MADA-Net, for fine-grained visual categorization. It addresses the challenges of inter-class similarities and scale variation through multiscale attention mechanisms and a dynamic aware module. A multiscale adjusted loss is also introduced to improve the network performance.
ELECTRONICS LETTERS
(2023)
Editorial Material
Gastroenterology & Hepatology
Xiao-jie Huang, Xiao-lu Lin, Wan-yin Deng
Article
Chemistry, Analytical
Xinxin Ji, Yi Xu, Zhongrui Wang, Xiaojie Huang, Guojing Xiao, Guang Yang, Wei Feng
Summary: Novel bifunctional nitrogen-doped carbon dots (N-CDs) were prepared through solid-phase thermal treatment of melamine and citric acid monohydrate (CA) as precursors. The N-CDs exhibited uniform size distribution, excellent photostability, and bright fluorescence emission with a fluorescence quantum yield of 24%. The N-CD-based fluorescence on-off-on sensing platform showed rapid, selective, and sensitive detection of TCs (tetracycline hydrochloride and doxycycline) and Al3+ with low detection limits.
MICROCHEMICAL JOURNAL
(2023)
Article
Cell Biology
Zhen-Chao Tang, Jiao-Jiao Liu, Xue-Tong Ding, Dan Liu, Hong-Wei Qiao, Xiao-Jie Huang, Hui Zhang, Jie Tian, Hong-Jun Li
Summary: Acquired immune deficiency syndrome infection can lead to cognitive dysfunction represented by changes in the default mode network. Longitudinal studies on the dynamic changes in the default mode network following infection and antiretroviral therapy are difficult to conduct directly in a clinic setting. Therefore, a longitudinal study was conducted in a rhesus monkey model to investigate the changes in default mode network connectivity over time.
NEURAL REGENERATION RESEARCH
(2023)
Article
Oncology
Fenglei Yu, Xiaojie Huang, Danting Zhou, Zhenyu Zhao, Fang Wu, Banglun Qian, Qiang Wang, Juan Chen, Qingchun Liang, Yi Jiang, Qi Ding, Qiongzhi He, Jingqun Tang, Xiang Wang, Wenliang Liu, Chen Chen
Summary: This study demonstrated, for the first time, the differences in genetic, epigenetic, and immune profiles between synchronous multiple primary lung cancers (sMPLC) and single primary lung cancer (SPLC). Compared to the similar genetic mutational landscape, the DNA methylation patterns and related immune profiles were significantly different between sMPLC and SPLC, indicating their essential roles in the initiation and development of sMPLC.
CLINICAL EPIGENETICS
(2023)
Article
Psychiatry
Jing Zhong, Xiao-Jie Huang, Xue-Mei Wang, Ming-Zhi Xu
Summary: This study aimed to examine whether distress tolerance mediates the relationship between stressful life events and suicide risk in patients with major depressive disorder (MDD). The study found that distress tolerance completely played a mediating role between stressful life events and suicide risk.
Article
Biochemistry & Molecular Biology
Xiaojie Huang, Linyan Jia, Yuanhui Jia, Xianghong Xu, Ruixue Wang, Mengtian Wei, Han Li, Hao Peng, Yingying Wei, Qizhi He, Kai Wang
Summary: Preeclampsia (PE) is a hypertensive disorder of pregnancy characterized by maternal endothelial dysfunction and end-organ damage. Exosomes derived from PE patients contain higher levels of soluble FMS-like tyrosine kinase-1 (sFlt-1) and induce endothelial dysfunction and PE development. The treatment with trophoblast-derived sFlt-1-enriched exosomes (sFlt-1-Exo) inhibits human umbilical vein endothelial cell (HUVEC) migration and tube formation, and increases sFlt-1 secretion through enhanced transcription in HUVECs. In pregnant mice, sFlt-1-Exo or recombinant mouse sFlt-1 treatment induces a preeclampsia-like phenotype.
Review
Gastroenterology & Hepatology
Menglu Liu, Hanying Qiu, Wenyan Zhang, Tingting Mei, Shan Tang, Yuxue Gao, Yanqiu Zhu, Xiaojie Huang, Haibin Yu
Summary: The study aimed to evaluate the diagnostic value of PRO-C3 as a biomarker for staging liver fibrosis. The results showed that PRO-C3, when used alone as a non-invasive biomarker, demonstrated clinically meaningful diagnostic accuracy for diagnosing the liver fibrosis stage in individuals with viral hepatitis or fatty liver disease.
JOURNAL OF GASTROENTEROLOGY AND HEPATOLOGY
(2023)
Article
Engineering, Biomedical
Bangze Zhang, Xiaoyan Wang, Lianggui Liu, Denghui Zhang, Xiaojie Huang, Ming Xia, Weiwei Jiang, Xiangsheng Huang
Summary: In this paper, we propose a correlation-enhanced lightweight network (CeLNet) for medical image segmentation. The network adopts a siamese structure for weight sharing and parameter saving. The proposed model achieves excellent segmentation performance with only 5.18M parameters, making it lightweight compared to other models.
PHYSICS IN MEDICINE AND BIOLOGY
(2023)
Article
Chemistry, Analytical
Yanru Zhao, Dongsheng Wang, Xiaojie Huang
Summary: Research on improving the precision of gas detection and developing effective search strategies was conducted based on a gas sensor array. The array was designed to mimic the artificial olfactory system and establish a one-to-one response mode to measured gases with inherent cross-sensitive properties. Quantitative identification algorithms were studied, and an improved Back Propagation algorithm combining cuckoo algorithm and simulated annealing algorithm was proposed. Test results demonstrated that the improved algorithm achieved the optimal solution with 0% error at the 424th iteration of the Schaffer function. The gas detection system designed using MATLAB effectively detected alcohol and methane concentrations within the detection range, exhibiting good performance.
Article
Oncology
Danting Zhou, Tianyu Yao, Xiaojie Huang, Fang Wu, Yi Jiang, Muyun Peng, Banglun Qian, Wenliang Liu, Fenglei Yu, Chen Chen
Summary: This study reported real-world data on the comprehensive diagnosis and treatment of patients with early-stage synchronous multiple primary lung cancers (sMPLC). The results showed that histopathological and radiological evaluation combined with genetic analyses could be a robust diagnostic approach for sMPLC. The Surgery + X treatment strategy demonstrated remarkable efficacy, superiority, and safety in the clinical treatment of early-stage sMPLC.
Article
Endocrinology & Metabolism
Yingjie Zhang, Yu Wang, Miao Liu, Lingge Wei, Jianmin Huang, Ziqian Dong, Meichao Guan, Weijie Wu, Jianqing Gao, Xiaojie Huang, Xin Guo, Peng Xie
Summary: This study aimed to explore the value of the FT4/TSH ratio in the etiological diagnosis of newly diagnosed patients with thyrotoxicosis. The study found that the FT4/TSH ratio can be used as a new reference index for the differential diagnosis of thyrotoxicosis.
FRONTIERS IN ENDOCRINOLOGY
(2023)
Article
Microbiology
Ting Sun, Yuchen Wang, Xiaoyu Song, Ruili Li, Fanghua Mei, Mengliu Yang, Xiaojie Huang, Yan Li, Xuwei Zhou, Haoyu Wang, Wendong Li, Jing Li, Lu Wang, Wei Shi, Kun Cai, Hongjun Li, Jing Zhang
Summary: Our study demonstrates the importance of B-cell activating humoral immunity in antibody production when encountering antigens. The effectiveness of inactivated SARS-CoV-2 vaccines varies among individuals, and understanding this differential response can aid in developing new vaccines for non-responders.
MICROBIOLOGY SPECTRUM
(2023)
