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A Multimodal Deep Learning-based Platform that Assists in the Classification of Lupus Subtypes and Differential Diagnosis among Lupus and Other Similar Skin Diseases

Research Square

引用

Research Square 2021年

作者： Lu, Qianjin Li, Qianwen Yang, Zhi Chen, Kaili Zhao, Ming Long, Hai Deng, Yueming Hu, Haoran Jia, Chen Wu, Meiyu Zhao, Zhidan Zhu, Huan Zhou, Suqing Zhao, Mingming Jiang, Yiqun Zhang, Wei Zhang, Bo He, Yanling Ran, Liwei Zhang, Chunlei Wu, Wenting Suolang, Quzong Luo, Hanhuan Kang, Xiaojing Wu, Caoying Jin, Hongzhong Chen, Lei Guo, Qing Gui, Guangji Li, Shanshan Si, Henan Guo, Shuping Liu, Hongye Liu, Xiguang Ma, Guozhang Deng, Danqi Yuan, Limei Lu, Jianyun Zeng, Jinrong Jiang, Xian Lyu, Xiaoyan Chen, Liuqing Hu, Bin Tao, Juan Liu, Yuhao Wang, Gang Zhu, Guannan Yao, Zhirong Xu, Qianyue Yang, Bin Wang, Yu Ding, Yan Yang, Xianxu Hu, Kai Wu, Haijing The Second Xiangya Hospital of Central South University China Department of Dermatology The Second Xiangya Hospital of Central South University China Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University China Institute of Dermatology Chinese Academy of Medical Sciences Peking Union Medical College China Second Xiangya Hospital of Central South University China Department of Dermatology Beijing Chao-yang Hospital Capital Medical University China Department of Dermatology Peking University Third Hospital China Department of Dermatology People's Hospital of Tibet Autonomous Region China Department of Dermatology People's Hospital of Xinjiang Uygur Autonomous Region China Department of Dermatology State Key Laboratory of Complex Severe and Rare Diseases Peking Union Medical College Hospital Chinese Academy of Medical Science Peking Union Medical College China Department of Dermatology Sun Yat-sen Memorial Hospital Sun Yat-sen University China Department of Dermatology The First Bethune Hospital of Jilin University China Department of Dermatology The First Hospital of Shanxi Medical University China Department of Dermatology The Hei Long Jiang Provincial Hospital China Second Affiliated Hospital of Kunming Medical College China Department of Dermatology The Third Xiangya Hospital Central South University China Department of Dermatology West China Hospital Sichuan University China Department of Dermatology Wuhan No.1 Hospital China Union Hospital Tongji Medical College HUST China Department of Dermatology Xijing Hospital Fourth Military Medical University Xi'An China University of California Davis United States Department of Dermatology Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine China Dermatology Hospital of Southern Medical University China Hainan Provincial Hospital of Skin Disease China Xiangtan University China Hunan Key Laboratory of Medical Epigenomics The Second

Lupus erythematosus (LE) has the reputation of being the Great Imitator due to its various cutaneous manifestations, which often lead to difficulties in accurate diagnosis and classification. Even with the assistance of deep learning systems (DLSs), clinical skin image-based single modalities still experience difficulty distinguishing subtypes of LE. Here, we present an application of multimodal DLS (MMDLS), in which the training database consists of clinical skin images, multi-immunohistochemistry (multi-IHC) images and the 2019 European League Against Rheumatism/American College of Rheumatology (EULAR/ACR) scores, to assist in the classification of LE subtypes (discoid LE (DLE), annular subacute cutaneous LE (A-SCLE), papulosquamous subacute cutaneous LE (P-SCLE), systemic LE (SLE)) and to aid in the differential diagnosis of other similar skin diseases. A total of 386 cases with 580 clinical skin images, 3380 multi-IHC images and 2019 EULAR/ACR scores from 25 institutions in China were included, and EfficientNet-B3 and ResNet-18 were utilized in this study. By comparing the single-modal and dual-modal DLS performances in multiple classifications, we demonstrate the superiority of the MMDLS (Ave-Sen = 0.9116, Ave-Spe = 0.9921, Ave-Pre = 0.9281). Moreover, the performance of MMDLS on 13 classifications was superior to that of dermatologists and pathologists (F1 score: dermatologists = 0.4471, pathologists = 0.6691, MMDLS = 0.9582) with an average area under the curve (AUC) of 0.9956. These results highlight the potential of the combined application of MMDLS and multi-IHC images to assist pathologists and dermatologists in diagnosing LE subtypes and similar skin diseases. © 2021, CC BY.

关键词： Classification (of information)

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Erratum: EFNet: evidence fusion network for tumor segmentation from PET-CT volumes (2021 Phys. Med. Biol.66 205005)

引用

Physics in medicine and biology 2021年第24期66卷

作者： Zhaoshuo Diao Huiyan Jiang Xian-Hua Han Yu-Dong Yao Tianyu Shi Software College Northeastern University Shenyang 110819 People's Republic of China. Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang 110819 People's Republic of China. Graduate School of Sciences and Technology for Innovation Yamaguchi University Yamaguchi-shi 7538511 Japan. Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken NJ 07030 United States of America.

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Automatic detection of ECG abnormalities by using an ensemble of deep residual networks with attention

arXiv

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arXiv 2019年

作者： Liu, Yang He, Runnan Wang, Kuanquan Li, Qince Sun, Qiang Zhao, Na Zhang, Henggui Harbin150001 China School of Physics and Astronomy University of Manchester ManchesterM13 9PL United Kingdom SPACEnter Space Science and Technology Institute Shenzhen518117 China International Laboratory for Smart Systems Key Laboratory of Intelligent of Computing in Medical Image Ministry of Education Northeastern University Shengyang110004 China Department of Pharmacology Beijing Electric Power Hospital Beijing100073 China

Heart disease is one of the most common diseases causing morbidity and mortality. Electrocardiogram (ECG) has been widely used for diagnosing heart diseases for its simplicity and non-invasive property. Automatic ECG analyzing technologies are expected to reduce human working load and increase diagnostic efficacy. However, there are still some challenges to be addressed for achieving this goal. In this study, we develop an algorithm to identify multiple abnormalities from 12-lead ECG recordings. In the algorithm pipeline, several preprocessing methods are firstly applied on the ECG data for denoising, augmentation and balancing recording numbers of variant classes. In consideration of efficiency and consistency of data length, the recordings are padded or truncated into a medium length, where the padding/truncating time windows are selected randomly to suppress overfitting. Then, the ECGs are used to train deep neural network (DNN) models with a novel structure that combines a deep residual network with an attention mechanism. Finally, an ensemble model is built based on these trained models to make predictions on the test data set. Our method is evaluated based on the test set of the First China ECG intelligent Competition dataset by using the F1 metric that is regarded as the harmonic mean between the precision and recall. The resultant overall F1 score of the algorithm is 0.875, showing a promising performance and potential for practical *** Codes 68T10 Copyright © 2019, The Authors. All rights reserved.

关键词： Electrocardiography

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Why is the Winner the Best?

Why is the Winner the Best?

引用

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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Convolutional restricted boltzmann machine based-radiomics for prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer

arXiv

引用

arXiv 2019年

作者： Wang, Li Wang, Lihui Chen, Qijian Sun, Caixia Cheng, Xinyu Zhu, Yuemin Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province College of Computer Science and Technology Guizhou University Guiyang550025 CNRS UMR5220 Inserm U1206 INSA Lyon Université de Lyon France

We proposed a novel convolutional restricted Boltzmann machine (CRBM)-based radiomic method for predicting pathologic complete response (pCR) to neoadjuvant chemotherapy treatment (NACT) in breast cancer. The method consists of extracting semantic features from CRBM network, and pCR prediction ⋯ It was evaluated on the dynamic contrastenhanced magnetic resonance imaging (DCE-MRI) data of 57 patients and using the area under the receiver operating characteristic curve (AUC). Traditional radiomics features and the semantic features learned from CRBM network were extracted from the images acquired before and after the administration of NACT. After the feature selection, the support vector machine (SVM), logistic regression (LR) and random forest (RF) were trained to predict the pCR status. Compared to traditional radiomic methods, the proposed CRBM-based radiomic method yielded an AUC of 0.92 for the prediction with the images acquired before and after NACT, and an AUC of 0.87 for the pretreatment prediction, which was increased by about 38%. The results showed that the CRBM-based radiomic method provided a potential means for accurately predicting the pCR to NACT in breast cancer before the treatment, which is very useful for making more appropriate and personalized treatment regimens. Copyright © 2019, The Authors. All rights reserved.

关键词： Forecasting

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Investigating EEG-Based Functional Connectivity Patterns for Multimodal Emotion Recognition

arXiv

引用

arXiv 2020年

作者： Wu, Xun Zheng, Wei-Long Lu, Bao-Liang Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Brain Science and Technology Research Center Qing Yuan Research Institute Shanghai Jiao Tong University 800 Dong Chuan Road Shanghai200240 China Clinical Data Animation Center Department of Neurology Massachusetts General Hospital Harvard Medical School 55 Fruit Street BostonMA United States

Compared with the rich studies on the motor brain-computer interface (BCI), the recently emerging affective BCI presents distinct challenges since the brain functional connectivity networks involving emotion are not well investigated. Previous studies on emotion recognition based on electroencephalography (EEG) signals mainly rely on single-channel-based feature extraction methods. In this paper, we propose a novel emotion-relevant critical subnetwork selection algorithm and investigate three EEG functional connectivity network features: strength, clustering coefficient, and eigenvector centrality. The discrimination ability of the EEG connectivity features in emotion recognition is evaluated on three public emotion EEG datasets: SEED, SEED-V, and DEAP. The strength feature achieves the best classification performance and outperforms the state-of-the-art differential entropy feature based on single-channel analysis for the EEG signals. The experimental results reveal that distinct functional connectivity patterns are exhibited for the five emotions of disgust, fear, sadness, happiness, and neutrality. Furthermore, we construct a multimodal emotion recognition model by combining the functional connectivity features from EEG and the features from eye movements or physiological signals using deep canonical correlation analysis. The classification accuracies of multimodal emotion recognition are 95.08 ± 6.42% on the SEED dataset, 84.51 ± 5.11% on the SEED-V dataset, and 85.34 ± 2.90% and 86.61 ± 3.76% for arousal and valence on the DEAP dataset, respectively. The results demonstrate the complementary representation properties of the EEG functional connectivity network features with eye movement data. In addition, we find that the brain networks constructed with fewer channels, i.e., 18 channels, achieve comparable performance with that of the 62-channel network with respect to multimodal emotion recognition and enable easier setups for BCI systems in real scenarios. Cop

关键词： Electrophysiology

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Local Temporal Joint Recurrence Common Spatial Patterns for MI-based BCI

Local Temporal Joint Recurrence Common Spatial Patterns for ...

引用

IEEE Information Technology, Networking, Electronic and Automation Control Conference

作者： Yue Deng Zhanli Li Haixian Wang Xuesong Lu Hui Fan Key Laboratory of Child Development and Learning Science of Ministry of Education School of Biological Science & Medical Engineering Southeast University Nanjing Jiangsu PR China College of Computer Science and Technology Xi'an University of Science and Technology Xi'an Shanxi PR China Zhongda Hospital Southeast University Nanjing Jiangsu PR China Co-innovation Center of Shandong Colleges and Universities: Future Intelligent Computing Shandong Technology and Business University Yantai Shandong PR China

ISBN: (数字)9781728143903

ISBN: (纸本)9781728143910

As a purely data-driven spatial domain filtering method, common spatial pattern (CSP) is widely used in motor imagery (MI)-based brain-computer interface (BCI) domains. However, the estimation of the covariance matrices in CSP is sensitive to noise and lacks local temporal information. To improve the performance of CSP, we propose a local temporal joint recurrence common spatial pattern (LTJRCSP), which considers temporally local information. Theoretically, LTJRCSP introduces the local temporal joint recurrence rate and therefore may contain more discriminative information and is more robust compared to CSP. After the feature is extracted, linear discriminant analysis (LDA) is then applied for classification. The experimental results, regardless of whether EEG dataset IIa of BCI Competition IV introduces outliers or not, justify the effectiveness of the proposed method.

关键词： Electroencephalography Covariance matrices Feature extraction Spatial filters Brain-computer interfaces Correlation Spatial databases

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Glioma grade predictions using scattering wavelet transform-based radiomics

arXiv

引用

arXiv 2019年

作者： Chen, Qijian Wang, Lihui Wang, Li Deng, Zeyu Zhang, Jian Zhu, Yuemin Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province College of Computer Science and Technology Guizhou University Guiyang550025 China CNRS UMR5220 Inserm U1206 Université de Lyon LyonSA 69000 France

Glioma grading before the surgery is very critical for the prognosis prediction and treatment plan making. In this paper, we present a novel scattering wavelet-based radiomics method to predict noninvasively and accurately the glioma grades. The multimodal magnetic resonance images of 285 patients were used, with the intratumoral and peritumoral regions well labeled. The wavelet scattering-based features and traditional radiomics features were firstly extracted from both intratumoral and peritumoral regions respectively. The support vector machine (SVM), logistic regression (LR) and random forest (RF) were then trained with 5-fold cross validation to predict the glioma grades. The prediction obtained with different features was finally evaluated in terms of quantitative metrics. The area under the receiver operating characteristic curve (AUC) of glioma grade prediction based on scattering wavelet features was up to 0.99 when considering both intratumoral and peritumoral features in multimodal images, which increases by about 17% compared to traditional radiomics. Such results shown that the local invariant features extracted from the scattering wavelet transform allows improving the prediction accuracy for glioma grading. In addition, the features extracted from peritumoral regions further increases the accuracy of glioma grading. Copyright © 2019, The Authors. All rights reserved.

关键词： Grading

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Robust unsupervised feature selection by nonnegative sparse subspace learning

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Neurocomputing 2019年 334卷 156-171页

作者： Wei Zheng Hui Yan Jian Yang School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing PR China School of Computer Engineering Jinling Institute of Technology Nanjing PR China PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Key Laboratory of Trusted Cloud Computing and Big Data Analysis Nanjing Xiaozhuang University Nanjing PR China

Sparse subspace learning has been demonstrated to be effective in data mining and machine learning. In this paper, we cast the unsupervised feature selection scenario as a matrix factorization problem from the viewpoint of sparse subspace learning. By minimizing the reconstruction residual, the learned feature weight matrix with the l 2,1 -norm and the non-negative constraints not only removes the irrelevant features, but also captures the underlying low dimensional structure of the data points. Meanwhile in order to enhance the model's robustness, l 1 -norm error function is used to resistant to outliers and sparse noise. An efficient iterative algorithm is introduced to optimize this non-convex and non-smooth objective function and the proof of its convergence is given. Although, there is a subtraction item in our multiplicative update rule, we validate its non-negativity. The superiority of our model is demonstrated by comparative experiments on various original datasets with and without malicious pollution.

关键词： Subspace learning Non-negative matrix factorization Unsupervised feature selection

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CMRxRecon2024: A Multi-Modality, Multi-View K-Space Dataset Boosting Universal Machine Learning for Accelerated Cardiac MRI

arXiv

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arXiv 2024年

作者： Wang, Zi Wang, Fanwen Qin, Chen Lyu, Jun Ouyang, Cheng Wang, Shuo Li, Yan Yu, Mengyao Zhang, Haoyu Guo, Kunyuan Shi, Zhang Li, Qirong Xu, Ziqiang Zhang, Yajing Li, Hao Hua, Sha Chen, Binghua Sun, Longyu Sun, Mengting Li, Qin Chu, Ying-Hua Bai, Wenjia Qin, Jing Zhuang, Xiahai Prieto, Claudia Young, Alistair Markl, Michael Wang, He Wu, Lianming Yang, Guang Qu, Xiaobo Wang, Chengyan Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Xiamen University Xiamen China Department of Bioengineering and Imperial-X Imperial College London London United Kingdom Cardiovascular Research Centre Royal Brompton Hospital London United Kingdom Department of Electrical and Electronic Engineering & Imperial-X Imperial College London London United Kingdom Psychiatry Neuroimaging Laboratory Brigham and Women’s Hospital Harvard Medical School Boston United States Department of Computing Department of Brain Sciences Imperial College London London United Kingdom Digital Medical Research Center School of Basic Medical Sciences Fudan University Shanghai China Department of Radiology Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China Human Phenome Institute Fudan University Shanghai China Department of Radiology Zhongshan Hospital Fudan University Shanghai China School of Health Science and Engineering University of Shanghai for Science and Technology Shanghai China GE Healthcare Beijing China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai China Department of Cardiovascular Medicine Ruijin Hospital Lu Wan Branch Shanghai Jiao Tong University School of Medicine Shanghai China Department of Radiology Ren Ji Hospital School of Medicine Shanghai Jiao Tong University Shanghai China Siemens Healthineers Ltd. Shanghai China School of Nursing The Hong Kong Polytechnic University Hong Kong School of Data Science Fudan University Shanghai China School of Engineering Pontificia Universidad Católica de Chile Santiago Chile School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom Millenium Institute for Intelligent Healthcare Engineering Santiago Chile Department of Radiology Feinberg School of Medicine Northwestern University Chicago Unit

The released CMRxRecon2024 dataset is currently the largest and most protocol-diverse publicly available k-space dataset including multi-modality and multi-view cardiac MRI data from 330 healthy volunteers, and each o... 详细信息

关键词： Adversarial machine learning

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