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A Strategy for Identifying the Risk of Developing Alzheimer's Disease from Healthy Controls: Distance to Novelty Detection Boundary

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SSRN

SSRN 2022年

作者： Mao, Jiangbing Ye, Qinyong Yang, Hongqin Bucholc, Magda Liu, Shuo Gao, Wenzhao Pan, Jie Xin, Jiawei Ding, Xuemei Fujian Provincial Key Laboratory for Photonics Technology Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education Fujian Normal University Fuzhou350007 China Department of Neurology Fujian Medical University Union Hospital Fuzhou350001 China School of Computing Engineering and Intelligent Systems Ulster University DerryNI LondonderryBT48 7JL United Kingdom Xiamen Jingyi Zhikang Medical Technology Co. Ltd. No. 59 Huli Avenue Huli District Xiamen361000 China

This study first employed four interpretable novelty detection (ND) algorithms to detect the severity of cognitive disorder of individuals. The detector was modeled based on the healthy controls (HC) data only. Datasets used are respectively from the Australian Imaging Biomarkers and Lifestyle Study of Ageing project and the Fujian medical University Union Hospital, China. A DtB (distance to ND boundary) strategy was proposed to visually detect the severity of cognitive decline of potential mild cognitive impairment (MCI) and Alzheimer’s disease (AD) from HC, in an early stage. With non-invasive and cost-effective cognitive and functional assessments modality, the best overall performance of detecting MCI&AD for the AIBL and FMUUH was obtained by using Mixture of Gaussian-based ND algorithm, with AUC of 0.8757 and 0.9443, respectively. More practically, an interactive web-based graphical user interface tool was further developed for non-technical stakeholders to easily utilize our proposed strategy for supporting MCI/AD diagnosis. © 2022, The Authors. All rights reserved.

关键词： Graphical user interfaces

Automated identification and quantification of myocardial inflammatory infiltration in digital histological images to diagnose myocarditis

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arXiv 2023年

作者： Liu, Yanyun Hua, Xiumeng Zhu, Shouping Wang, Congrui Chen, Xiao Shi, Yu Song, Jiangping Zhou, Weihua School of Life Science and Technology Xidian University Engineering Research Center of Molecular and Neuro Imaging Ministry of Education Shaanxi Xi'An China Xi'an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment School of Life Science and Technology Xidian University Shaanxi Xi'An China Innovation Center for Advanced Medical Imaging and Intelligent Medicine Guangzhou Institute of Technology Xidian University Guangdong Guangzhou China Beijing Key Laboratory of Preclinical Research and Evaluation for Cardiovascular Implant Materials Animal Experimental Centre Fuwai Hospital National Centre for Cardiovascular Disease Chinese Academy of Medical Sciences Peking Union Medical College Beijing China State Key Laboratory of Cardiovascular Disease Fuwai Hospital National Center for Cardiovascular Diseases Chinese Academy of Medical Sciences Peking Union Medical College 167A Beilishi Road Xi Cheng District Beijing China Department of Cardiovascular Surgery Fuwai Hospital National Center for Cardiovascular Diseases National Clinical Research Center of Cardiovascular Diseases State Key Laboratory of Cardiovascular Disease Chinese Academy of Medical Sciences Peking Union Medical College Beijing China The Cardiomyopathy Research Group Fuwai Hospital Beijing China Department of Applied Computing Michigan Technological University HoughtonMI United States Center for Biocomputing and Digital Health Institute of Computing and Cybersystems Health Research Institute Michigan Technological University HoughtonMI United States

Background Given the dimensionality of WSIs and the increase in the number of potential cases, the manual histological diagnosis of myocarditis based on Dallas Criteria is time-consuming, experience-dependent. Objectives To overcome the limitations of manual diagnosis methods, this study aims to develop a new computational pathology approach that automates the identification and quantification of myocardial inflammatory infiltration in digital hematoxylin and eosin (HE)-stained images to assist pathologists in diagnosing myocarditis. Methods 898 HE-stained whole slide images (WSIs) of myocardium from 154 heart transplant patients diagnosed with myocarditis or dilated cardiomyopathy (DCM) were included in this study. An automated deep-learning (DL)-based computational pathology approach was developed to identify nuclei and detect myocardial inflammatory infiltration, enabling the quantification of the lymphocyte nuclear density (LND) on myocardial WSIs. A cutoff value based on the quantification of LND was proposed to determine if the myocardial inflammatory infiltration was present. The performance of our approach was evaluated with a five-fold cross-validation experiment, tested with an internal test set from the myocarditis group, and confirmed by an external test from a double-blind trial group. Results An LND of 1.02/mm2 could distinguish WSIs with myocarditis from those without. The accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) in the five-fold cross-validation experiment were 0.899 ± 0.035, 0.971 ± 0.017, 0.728 ± 0.073 and 0.849 ± 0.044, respectively. For the internal test set, the accuracy, sensitivity, specificity, and AUC were 0.887, 0.971, 0.737, and 0.854, respectively. The accuracy, sensitivity, specificity, and AUC for the external test set reached 0.853, 0.846, 0.858, and 0.852, respectively. Conclusion Our new approach provides accurate and reliable quantification of the LND of myocardial WSIs bas

关键词： Pathology

Self-Evolutionary Neuron Model for Fast-Response Spiking Neural Networks

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TechRxiv

TechRxiv 2019年

作者： Zhang, Anguo Han, Ying Hu, Jing Niu, Yuzhen Gao, Yueming Chen, Zhizhang Zhao, Kai College of Physics and Information Engineering Fuzhou University Fujian350108 China The Key Laboratory of Medical Instrumentation Pharmaceutical Technology of Fujian Province Fuzhou350116 China Research Institute of Ruijie Ruijie Networks Co. Ltd Fujian350002 China School of Public Health Xiamen University Xiamen361102 China College of Information and Intelligent Transportation Fujian Chuanzheng Communications College Fuzhou350007 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing College of Mathematics and Computer Science Fuzhou University The Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fujian350108 China College of Physics and information Engineering Fuzhou University the Key Laboratory of Medical Instrumentation Pharmaceutical Technology of Fujian Province Fujian350108 China College of Physics and Information Engineering Fuzhou University 350108 China Faculty of Science and Technology University of Macau 999078 China

We propose two simple and effective spiking neuron models to improve the response time of the conventional spiking neural network. The proposed neuron models adaptively tune the presynaptic input current depending on the input received from its presynapses and subsequent neuron firing events. We analyze and derive the firing activity homeostatic convergence of the proposed models. We experimentally verify and compare the models on MNIST handwritten digits and FashionMNIST classification tasks. We show that the proposed neuron models significantly increase the response speed to the input signal. Experiment codes are available at https://***/anvien/Evol-SNN. © 2019, CC0.

关键词： Neural networks

Deep learning radiomics model related with genomics phenotypes for pathological complete response prediction in locally advanced rectal cancer under neoadjuvant chemoradiotherapy

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Deep learning radiomics model related with genomics phenotyp...

IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Ye Wang Zhen Pan Lanyan Lin Huajun Cai Feng Ding Chaojun Tan Dingyuan Zheng Yajun Xie Guoxian Guan Department of Colorectal Surgery The First Affiliated Hospital of Fujian Medical University Fuzhou China Department of Geriatric Medicine Fujian Key Laboratory of Geriatrics Diseases Fujian Provincial Center for Geriatrics Fuzhou University Affiliated Provincial Hospital Shengli Clinical Medical College of Fujian Medical University Fuzhou China Fujian Huaye Information Technology Co. Ltd China School of Big Data \& Key Laboratory of Data Science and Intelligent Computing Fuzhou University of International Studies and Trade Fuzhou China Department of Colorectal Surgery The First Affiliated Hospital of Fujian Medical University National Regional Medical Center Binhai Campus of the First Affiliated Hospital Fuzhou China

ISBN: (数字)9798350356670

ISBN: (纸本)9798350356687

Background: Developing a treatment strategy after neoadjuvant chemoradiotherapy (nCRT) based on accurate pathological complete response (pCR) predictions is crucial for locally advanced rectal cancer (LARC).Objective: To determine the effectiveness of a radiomics model using diffusion-weighted imaging (DWI) and T2-weighted in predicting post-nCRT pCR efficacy after ***: This study enrolled 237 LARC patients who had radical surgery under nCRT between February 2020 and August 2023. Tumor tissues from 54 patients were RNA sequenced. Radiomics features from preoperative MRI data were used to develop a model for deep learning via the LASSO algorithm. ROC curves were used to assess the model performance. Following Spearman's correlation, analyses of transcriptomic data were conducted to identify the genes correlated with the DPL ***: The DPL model, incorporating 7 features and 5 machine-learning algorithms, was used to create risk assessment models. The RF model excelled in predicting pCR, achieving an AUC of 0.91, accuracy of 0.88, and F1 score of 0.89. Furthermore, catabolic processes and pathways related to immunity were associated with selected DPL ***: The modified MRI-based radiomics model shows good predictive efficacy in predicting pCR after nCRT in LARC patients. Additionally, we investigated the genomic phenotypes associated with DPL.

关键词： Deep learning Support vector machines Pathology Analytical models Transcriptomics Predictive models Data models Random forests Radiomics Cancer

Cost-sensitive boosting pruning Trees for depression detection on Twitter

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arXiv 2019年

作者： Tong, Lei Liu, Zhihua Jiang, Zheheng Zhou, Feixiang Chen, Long Lyu, Jialin Zhang, Xiangrong Zhang, Qianni Sadka, Abdul Wang, Yinhai Li, Ling Zhou, Huiyu School Of Computing And Mathematical Sciences University Of Leicester United Kingdom Key Laboratory Of Intelligent Perception And Image Understanding Of Ministry Of Education Of China Xidian University China School Of Electronic Engineering And Computer Science Queen Mary University Of London United Kingdom Centre For Media Comms Research Brunel University London United Kingdom Discovery Sciences AstraZeneca R&D Darwin Building Cambridge Science Park CambridgeCB4 0WG United Kingdom School Of Computing University Of Kent United Kingdom

Depression is one of the most common mental health disorders, and a large number of depressed people commit suicide each year. Potential depression sufferers usually do not consult psychological doctors because they feel ashamed or are unaware of any depression, which may result in severe delay of diagnosis and treatment. In the meantime, evidence shows that social media data provides valuable clues about physical and mental health conditions. In this paper, we argue that it is feasible to identify depression at an early stage by mining online social behaviours. Our approach, which is innovative to the practice of depression detection, does not rely on the extraction of numerous or complicated features to achieve accurate depression detection. Instead, we propose a novel classifier, namely, Cost-sensitive Boosting Pruning Trees (CBPT), which demonstrates a strong classification ability on two publicly accessible Twitter depression detection datasets. To comprehensively evaluate the classification capability of CBPT, we use additional three datasets from the UCI machine learning repository and CBPT obtains appealing classification results against several state of the arts boosting algorithms. Finally, we comprehensively explore the influence factors for the model prediction, and the results manifest that our proposed framework is promising for identifying Twitter users with depression. Copyright © 2019, The Authors. All rights reserved.

关键词： Social networking (online)

The softness of tumour-cell-derived microparticles regulates their drug-delivery efficiency (vol 3, pg 729, 2019)

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NATURE BIOmedical ENGINEERING 2021年第5期5卷 481-481页

作者： Liang, Qingle Bie, Nana Yong, Tuying Tang, Ke Shi, Xiaolong Wei, Zhaohan Jia, Haibo Zhang, Xiaoqiong Zhao, Haiyan Huang, Wei Gan, Lu Huang, Bo Yang, Xiangliang National Engineering Research Center for Nanomedicine College of Life Science and Technology Huazhong University of Science and Technology Wuhan China Hubei Key Laboratory of Bioinorganic Chemistry and Materia Medica Huazhong University of Science and Technology Wuhan China Department of Biochemistry and Molecular Biology Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Immunology and National Key Laboratory of Medical Molecular Biology Institute of Basic Medical Sciences Chinese Academy of Medical Sciences Peking Union Medical College Beijing China Institute of Computing Science and Technology Guangzhou University Guangzhou China School of Artificial Intelligent and Automation Huazhong University of Science and Technology Wuhan China Key Laboratory of Molecular Biophysics of Ministry of Education College of Life Science and Technology Huazhong University of Science and Technology Wuhan China

A Correction to this paper has been published: https://***/10.1038/s41551-021-00694-0.

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3DGR-CAR: Coronary artery reconstruction from ultra-sparse 2D X-ray views with a 3D Gaussians representation

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arXiv 2024年

作者： Fu, Xueming Li, Yingtai Tang, Fenghe Li, Jun Zhao, Mingyue Teng, Gao-Jun Kevin Zhou, S. Anhui Hefei230026 China Suzhou Institute for Advance Research USTC Jiangsu Suzhou215123 China Key Laboratory of Precision and Intelligent Chemistry USTC Anhui Hefei230026 China Institute of Computing Technology CAS China Center of Interventional Radiology & Vascular Surgery Department of Radiology Medical School Zhongda Hospital Southeast University Nanjing210009 China

Reconstructing 3D coronary arteries is important for coronary artery disease diagnosis, treatment planning and operation navigation. Traditional reconstruction techniques often require many projections, while reconstruction from sparse-view X-ray projections is a potential way of reducing radiation dose. However, the extreme sparsity of coronary arteries in a 3D volume and ultra-limited number of projections pose significant challenges for efficient and accurate 3D reconstruction. To this end, we propose 3DGR-CAR, a 3D Gaussian Representation for Coronary Artery Reconstruction from ultra-sparse X-ray projections. We leverage 3D Gaussian representation to avoid the inefficiency caused by the extreme sparsity of coronary artery data and propose a Gaussian center predictor to overcome the noisy Gaussian initialization from ultra-sparse view projections. The proposed scheme enables fast and accurate 3D coronary artery reconstruction with only 2 views. Experimental results on two datasets indicate that the proposed approach significantly outperforms other methods in terms of voxel accuracy and visual quality of coronary arteries. The code will be available in https://***/windrise/3DGR-CAR. Copyright © 2024, The Authors. All rights reserved.

关键词： Gaussian distribution

WeakSurg: Weakly supervised surgical instrument segmentation using temporal equivariance and semantic continuity

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arXiv 2024年

作者： Wang, Qiyuan Liu, Yanzhe Zhao, Shang Liu, Rong Zhou, S. Kevin Anhui Hefei230026 China Suzhou Institute for Advanced Research USTC Jiangsu Suzhou215123 China Key Laboratory of Precision and Intelligent Chemistry USTC Anhui Hefei230026 China Institute of Computing Technology CAS Beijing100190 China Faculty of Hepato-Biliary-Pancreatic Surgery The First Medical Center Chinese PLA General Hospital Beijing China

For robotic surgical videos, instrument presence annotations are typically recorded with video streams, which offering the potential to reduce the manually annotated costs for segmentation. However, weakly supervised surgical instrument segmentation with only instrument presence labels has been rarely explored in surgical domain due to the highly underconstrained challenges. Temporal properties can enhance representation learning by capturing sequential dependencies and patterns over time even in incomplete supervision situations. From this, we take the inherent temporal attributes of surgical video into account and extend a two-stage weakly supervised segmentation paradigm from different perspectives. Firstly, we make temporal equivariance constraint to enhance pixel-wise temporal consistency between adjacent features. Secondly, we constrain class-aware semantic continuity between global and local regions across temporal dimension. Finally, we generate temporal-enhanced pseudo masks from consecutive frames to suppress irrelevant regions. Extensive experiments are validated on two surgical video datasets, including one cholecystectomy surgery benchmark and one real robotic left lateral segment liver surgery dataset. We annotate instance-wise instrument labels with fixed time-steps which are double checked by a clinician with 3-years experience to evaluate segmentation results. Experimental results demonstrate the promising performances of our method, which consistently achieves comparable or favorable results with previous state-of-the-art approaches. Copyright © 2024, The Authors. All rights reserved.

关键词： Robotic surgery

Multi-Granularity Prosodic Speech Synthesis with Grammar Information

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SSRN

SSRN 2024年

作者： Zhou, Jian Liu, Jiahui Fan, Cunhang Zheng, Wenming Lv, Zhao Tao, Liang Kwan, Hon Keung The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Computer Science and Technology Anhui University Hefei230601 China Ministry of Education School of Biological Science and Medical Engineering Southeast University Nanjing210096 China The Department of Electrical and Computer Engineering University of Windsor WindsorN9B 3P4 Canada

Personalized speech synthesis involves learning an individual's unique speaking patterns from a reference audio to generate speech that mimics the speaker's habitual rhythm. However, the one-to-many mapping between text and speech, combined with the dynamic nature of personalized speech style characteristics in the reference audio, poses significant challenges for accurate personalized speech synthesis. In this paper, we propose a Grammar Infused Multi-granularity Prosody Network (GMG ProsodyNet) for personalized speech synthesis. Specifically, 1) We model prosodic features hierarchically at the levels of utterance, content syntax, word, and phoneme. We utilize the utterance-level prosodic feature to guide the prediction of fine-grained prosodic features according to the hierarchical property of prosodic features. We introduce a context syntax encoder to improve the prediction accuracy of duration, pitch, and energy characteristics in the synthesized audio. The proposed word-level prosodic encoder can efficiently extract valuable pitch dynamic and speech continuity features from the word spectrum. For controlling subtle prosodic nuances, we employ phoneme-level prosodic modeling. 2) We extract syntax level prosodic features from the syntax graph constructed by Graph Neural Network (GNN) with word as node, exploiting grammatical dependencies between distant words. Experimental results demonstrate that the proposed GMG ProsodyNet can effectively encode delicate and personalized prosodic features, leading to improved speech synthesis quality, fluency, and naturalness. © 2024, The Authors. All rights reserved.

关键词： Speech synthesis