Deep neural networks have revolutionized medical image analysis and disease diagnosis. Despite their impressive performance, it is difficult to generate well-calibrated probabilistic outputs for such networks, which makes them uninterpretable black boxes. Bayesian neural networks provide a principled approach for modelling uncertainty and increasing patient safety, but they have a large computational overhead and provide limited improvement in calibration. In this work, by taking skin lesion classification as an example task, we show that by shifting Bayesian inference to the functional space we can craft meaningful priors that give better calibrated uncertainty estimates at a much lower computational cost.
One way to improve the estimation of time varying channels is to incorporate knowledge of previous observations. In this context, Dynamical VAEs (DVAEs) build a promising deep learning (DL) framework which is well suited to learn the distribution of time series data. We introduce a new DVAE architecture, called k-MemoryMarkovVAE (k-MMVAE), whose sparsity can be controlled by an additional memory parameter. Following the approach in [1] we derive a k-MMVAE aided channel estimator which takes temporal correlations of successive observations into account. The results are evaluated on simulated ch…
An ordinary differential equation (ODE) model, whose regression curves are a set of solution curves for some ODEs, poses a challenge in parameter estimation. The challenge, due to the frequent absence of analytic solutions and the complicated likelihood surface, tends to be more severe especially for larger models with many parameters and variables. Yang and Lee (2021) proposed a state-space model with variational Bayes (SSVB) for ODE, capable of fast and stable estimation in somewhat large ODE models. The method has shown excellent performance in parameter estimation but has a weakness of und…
Jawaharlal Nehru Technological University（贾瓦哈拉尔·尼赫鲁科技大学）；International Centre for Interdisciplinary Research and Innovation, VNRVJIET Campus（VNRVJIET校区国际跨学科研究与创新中心）；National Remote Sensing Center (ISRO)（国家遥感中心（ISRO））
Identification of the influential clinical symptoms and laboratory features that help in the diagnosis of dengue fever in early phase of the illness would aid in designing effective public health management and virological surveillance strategies. Keeping this as our main objective we develop in this paper, a new computational intelligence based methodology that predicts the diagnosis in real time, minimizing the number of false positives and false negatives. Our methodology consists of three major components (i) a novel missing value imputation procedure that can be applied on any data set co…
Local Interpretable Model-Agnostic Explanations (LIME) is a popular technique used to increase the interpretability and explainability of black box Machine Learning (ML) algorithms. LIME typically generates an explanation for a single prediction by any ML model by learning a simpler interpretable model (e.g. linear classifier) around the prediction through generating simulated data around the instance by random perturbation, and obtaining feature importance through applying some form of feature selection. While LIME and similar local algorithms have gained popularity due to their simplicity, t…
Center for Advanced Technology and Education（先进技术与教育中心）；Florida International University（佛罗里达国际大学）
Multiple modalities of biomarkers have been proved to be very sensitive in assessing the progression of Alzheimer's disease (AD), and using these modalities and machine learning algorithms, several approaches have been proposed to assist in the early diagnosis of AD. Among the recent investigated state-of-the-art approaches, Gaussian discriminant analysis (GDA)-based approaches have been demonstrated to be more effective and accurate in the classification of AD, especially for delineating its prodromal stage of mild cognitive impairment (MCI). Moreover, among those binary classification invest…
Centre for Big Data Research in Health, University of New South Wales（新南威尔士大学健康大数据研究中心）
Deep neural networks are increasingly being used for computer-aided diagnosis, but erroneous diagnoses can be extremely costly for patients. We propose a learning to defer with uncertainty (LDU) algorithm which identifies patients for whom diagnostic uncertainty is high and defers them for evaluation by human experts. LDU was evaluated on the diagnosis of myocardial infarction (using discharge summaries), the diagnosis of any comorbidities (using structured data), and the diagnosis of pleural effusion and pneumothorax (using chest x-rays), and compared with 'learning to defer without uncertain…
Beijing Institute of Technology（北京理工大学）；Xuanwu Hospital, Capital Medical University（首都医科大学宣武医院）；National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（中国医学科学院北京协和医学院肿瘤医院（国家癌症中心/国家肿瘤临床医学研究中心））
Current Computer-Aided Diagnosis (CAD) methods mainly depend on medical images. The clinical information, which usually needs to be considered in practical clinical diagnosis, has not been fully employed in CAD. In this paper, we propose a novel deep learning-based method for fusing Magnetic Resonance Imaging (MRI)/Computed Tomography (CT) images and clinical information for diagnostic tasks. Two paths of neural layers are performed to extract image features and clinical features, respectively, and at the same time clinical features are employed as the attention to guide the extraction of imag…
Vrije Universiteit（阿姆斯特丹自由大学）；Amsterdam UMC（阿姆斯特丹大学医学中心）；Amsterdam Neuroscience（阿姆斯特丹神经科学研究所）；Erasmus MC（伊拉斯姆斯医学中心）；University College London（伦敦大学学院）；TU Delft（代尔夫特理工大学）
Computer-aided methods have shown added value for diagnosing and predicting brain disorders and can thus support decision making in clinical care and treatment planning. This chapter will provide insight into the type of methods, their working, their input data - such as cognitive tests, imaging and genetic data - and the types of output they provide. We will focus on specific use cases for diagnosis, i.e. estimating the current 'condition' of the patient, such as early detection and diagnosis of dementia, differential diagnosis of brain tumours, and decision making in stroke. Regarding predic…
We introduce a new dataset called Synthetic COVID-19 Chest X-ray Dataset for training machine learning models. The dataset consists of 21,295 synthetic COVID-19 chest X-ray images to be used for computer-aided diagnosis. These images, generated via an unsupervised domain adaptation approach, are of high quality. We find that the synthetic images not only improve performance of various deep learning architectures when used as additional training data under heavy imbalance conditions, but also detect the target class with high confidence. We also find that comparable performance can also be achi…
Low grade endometrial stromal sarcoma (LGESS) is rare form of cancer, accounting for about 0.2% of all uterine cancer cases. Approximately 75% of LGESS patients are initially misdiagnosed with leiomyoma, which is a type of benign tumor, also known as fibroids. In this research, uterine tissue biopsy images of potential LGESS patients are preprocessed using segmentation and staining normalization algorithms. A variety of classic machine learning and leading deep learning models are then applied to classify tissue images as either benign or cancerous. For the classic techniques considered, the h…
Huazhong University of Science and Technology（华中科技大学）；Guizhou Normal University（贵州师范大学）；Hubei University（湖北大学）
Mapping X-ray images, radiology reports, and other medical data as binary codes in the common space, which can assist clinicians to retrieve pathology-related data from heterogeneous modalities (i.e., hashing-based cross-modal medical data retrieval), provides a new view to promot computeraided diagnosis. Nevertheless, there remains a barrier to boost medical retrieval accuracy: how to reveal the ambiguous semantics of medical data without the distraction of superfluous information. To circumvent this drawback, we propose Deep Supervised Information Bottleneck Hashing (DSIBH), which effectivel…
Universitat Pompeu Fabra（庞培法布拉大学）；BCN MedTech（巴塞罗那医疗科技中心）；Queen Mary University of London（伦敦玛丽女王大学）；William Harvey Research Institute（威廉·哈维研究所）；Stanford University（斯坦福大学）；Catalan Institution for Research and Advanced Studies (ICREA)（加泰罗尼亚研究与高级研究所）
Use expert visualization or conventional clinical indices can lack accuracy for borderline classications. Advanced statistical approaches based on eigen-decomposition have been mostly concerned with shape and motion indices. In this paper, we present a new approach to identify CVDs from cine-MRI by estimating large pools of radiomic features (statistical, shape and textural features) encoding relevant changes in anatomical and image characteristics due to CVDs. The calculated cine-MRI radiomic features are assessed using sequential forward feature selection to identify the most relevant ones f…
Amirkabir University of Technology（阿米尔卡比尔理工大学）；Atieh Clinical Neuroscience Center（阿提耶临床神经科学中心）
The generalization and robustness of an electroencephalogram (EEG)-based computer aided diagnostic system are crucial requirements in actual clinical practice. To reach these goals, we propose a new EEG representation that provides a more realistic view of brain functionality by applying multi-instance (MI) framework to consider the non-stationarity of the EEG signal. The non-stationary characteristic of EEG is considered by describing the signal as a bag of relevant and irrelevant concepts. The concepts are provided by a robust representation of homogenous segments of EEG signal using spatial…
Universidad Técnica Particular de Loja（洛哈技术大学）；Instituto de Instrumentacion para la Imagen Molecular I3M, Universitat Politécnica de Valencia（瓦伦西亚理工大学分子图像仪器研究所I3M）；University of Waterloo（滑铁卢大学）
This paper provides a critical review of the literature on deep learning applications in breast tumor diagnosis using ultrasound and mammography images. It also summarizes recent advances in computer-aided diagnosis (CAD) systems, which make use of new deep learning methods to automatically recognize images and improve the accuracy of diagnosis made by radiologists. This review is based upon published literature in the past decade (January 2010 January 2020). The main findings in the classification process reveal that new DL-CAD methods are useful and effective screening tools for breast cance…
College of Physics and Information Engineering, Fuzhou University（福州大学物理与信息工程学院）；Thoracic Department, Fujian Medical University Union Hospital（福建医科大学附属协和医院胸外科）
Background and Objective:Computer-aided diagnosis (CAD) systems promote diagnosis effectiveness and alleviate pressure of radiologists. A CAD system for lung cancer diagnosis includes nodule candidate detection and nodule malignancy evaluation. Recently, deep learning-based pulmonary nodule detection has reached satisfactory performance ready for clinical application. However, deep learning-based nodule malignancy evaluation depends on heuristic inference from low-dose computed tomography volume to malignant probability, which lacks clinical cognition. Methods:In this paper, we propose a joint…
Northeastern University（东北大学）；China Medical University（中国医科大学）；University of Luebeck（吕贝克大学）
The gold standard for gastric cancer detection is gastric histopathological image analysis, but there are certain drawbacks in the existing histopathological detection and diagnosis. In this paper, based on the study of computer aided diagnosis system, graph based features are applied to gastric cancer histopathology microscopic image analysis, and a classifier is used to classify gastric cancer cells from benign cells. Firstly, image segmentation is performed, and after finding the region, cell nuclei are extracted using the k-means method, the minimum spanning tree (MST) is drawn, and graph …
Hong Kong Baptist University（香港浸会大学）；Harbin Institute of Technology（哈尔滨工业大学）；The University of Hong Kong - Shenzhen Hospital（香港大学深圳医院）；Shenzhen Yiyuan Intelligent Technology Co., Ltd.（深圳怡园智能科技有限公司）
Skin disease is one of the most common types of human diseases, which may happen to everyone regardless of age, gender or race. Due to the high visual diversity, human diagnosis highly relies on personal experience; and there is a serious shortage of experienced dermatologists in many countries. To alleviate this problem, computer-aided diagnosis with state-of-the-art (SOTA) machine learning techniques would be a promising solution. In this paper, we aim at understanding the performance of convolutional neural network (CNN) based approaches. We first build two versions of skin disease datasets…
Xi’an Jiaotong University（西安交通大学）；The First Affiliated Hospital of Xi’an Jiaotong University（西安交通大学第一附属医院）；SenseTime Research（商汤科技研究院）
Electrocardiogram (ECG) signals play critical roles in the clinical screening and diagnosis of many types of cardiovascular diseases. Despite deep neural networks that have been greatly facilitated computer-aided diagnosis (CAD) in many clinical tasks, the variability and complexity of ECG in the clinic still pose significant challenges in both diagnostic performance and clinical applications. In this paper, we develop a robust and scalable framework for the clinical recognition of ECG. Considering the fact that hospitals generally record ECG signals in the form of graphic waves of 2-D images,…
German Research Center for Artificial Intelligence (DFKI) GmbH（德国人工智能研究中心）；Technical University Kaiserslautern（凯泽斯劳滕工业大学）；University Hospital Münster（明斯特大学医院）；University Hospital of Düsseldorf（杜塞尔多夫大学医院）；School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST)（国立科技大学电气工程与计算机科学学院）；Deep Learning Laboratory, National Center of Artificial Intelligence（国家人工智能中心深度学习实验室）
One principal impediment in the successful deployment of AI-based Computer-Aided Diagnosis (CAD) systems in clinical workflows is their lack of transparent decision making. Although commonly used eXplainable AI methods provide some insight into opaque algorithms, such explanations are usually convoluted and not readily comprehensible except by highly trained experts. The explanation of decisions regarding the malignancy of skin lesions from dermoscopic images demands particular clarity, as the underlying medical problem definition is itself ambiguous. This work presents ExAID (Explainable AI f…
Shenzhen University（深圳大学）；Guangdong Province Traditional Chinese Medical Hospital（广东省中医院）
Automated breast ultrasound (ABUS) is a new and promising imaging modality for breast cancer detection and diagnosis, which could provide intuitive 3D information and coronal plane information with great diagnostic value. However, manually screening and diagnosing tumors from ABUS images is very time-consuming and overlooks of abnormalities may happen. In this study, we propose a novel two-stage 3D detection network for locating suspected lesion areas and further classifying lesions as benign or malignant tumors. Specifically, we propose a 3D detection network rather than frequently-used segme…
A ChatGPT Aided Explainable Framework for Zero-Shot Medical Image Diagnosis
Zhejiang University-University of Illinois at Urbana-Champaign Institute（浙江大学伊利诺伊大学厄巴纳香槟校区联合学院）；Zhejiang University（浙江大学）；National University of Singapore（新加坡国立大学）；Angelalign Inc.（时代天使公司）
The past years have seen a considerable increase in cancer cases. However, a cancer diagnosis is often complex and depends on the types of images provided for analysis. It requires highly skilled practitioners but is often time-consuming and error-prone. If Machine Learning and deep learning algorithms have been widely used, a comprehensive review of the techniques used from the pre-processing steps to the final prediction is lacking. With this review, we aim to provide a comprehensive overview of the current steps required in building efficient and accurate machine learning algorithm for canc…
Keen Eye（基恩眼公司）；LTCI, Télécom Paris, Institut Polytechnique de Paris（巴黎理工学院巴黎电信学院LTCI实验室）
Deep learning methods are widely used for medical applications to assist medical doctors in their daily routines. While performances reach expert's level, interpretability (highlight how and what a trained model learned and why it makes a specific decision) is the next important challenge that deep learning methods need to answer to be fully integrated in the medical field. In this paper, we address the question of interpretability in the context of whole slide images (WSI) classification. We formalize the design of WSI classification architectures and propose a piece-wise interpretability app…
University of Central Florida（中佛罗里达大学）；National Institutes of Health（美国国立卫生研究院）
There are at least two categories of errors in radiology screening that can lead to suboptimal diagnostic decisions and interventions:(i)human fallibility and (ii)complexity of visual search. Computer aided diagnostic (CAD) tools are developed to help radiologists to compensate for some of these errors. However, despite their significant improvements over conventional screening strategies, most CAD systems do not go beyond their use as second opinion tools due to producing a high number of false positives, which human interpreters need to correct. In parallel with efforts in computerized analy…
Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院）；The First Affiliated Hospital of Guangzhou Medical University（广州医科大学附属第一医院）；State Key Laboratory of Respiratory Disease（呼吸疾病国家重点实验室）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；University of Chinese Academy of Sciences（中国科学院大学）
Lung cancer is the commonest cause of cancer deaths worldwide, and its mortality can be reduced significantly by performing early diagnosis and screening. Since the 1960s, driven by the pressing needs to accurately and effectively interpret the massive volume of chest images generated daily, computer-assisted diagnosis of pulmonary nodule has opened up new opportunities to relax the limitation from physicians' subjectivity, experiences and fatigue. And the fair access to the reliable and affordable computer-assisted diagnosis will fight the inequalities in incidence and mortality between popul…
Beijing Institute of Technology（北京理工大学）；Université 8 Mai 1945 Guelma（盖勒马8 Mai 1945大学）；National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（中国医学科学院北京协和医学院肿瘤医院国家癌症中心国家肿瘤临床研究中心）
Mammography remains the most prevalent imaging tool for early breast cancer screening. The language used to describe abnormalities in mammographic reports is based on the breast Imaging Reporting and Data System (BI-RADS). Assigning a correct BI-RADS category to each examined mammogram is a strenuous and challenging task for even experts. This paper proposes a new and effective computer-aided diagnosis (CAD) system to classify mammographic masses into four assessment categories in BI-RADS. The mass regions are first enhanced by means of histogram equalization and then semiautomatically segment…
University College London（伦敦大学学院）；Scuola Superiore Sant’Anna（圣安娜高等研究院）；Universitat Autònoma de Barcelona（巴塞罗那自治大学）；Toshiba Corporate Research and Development Center（东芝企业研发中心）；The Royal Infirmary of Edinburgh（爱丁堡皇家医院）；University of Turin（都灵大学）
Early diagnosis is essential for the successful treatment of bowel cancers including colorectal cancer (CRC) and capsule endoscopic imaging with robotic actuation can be a valuable diagnostic tool when combined with automated image analysis. We present a deep learning rooted detection and segmentation framework for recognizing lesions in colonoscopy and capsule endoscopy images. We restructure established convolution architectures, such as VGG and ResNets, by converting them into fully-connected convolution networks (FCNs), fine-tune them and study their capabilities for polyp segmentation and…
Technische Universität Kaiserslautern（凯泽斯劳滕工业大学）；German Research Center for Artificial Intelligence GmbH (DFKI)（德国人工智能研究中心）；Opthalmology Clinic（眼科诊所）；National University of Science and Technology (NUST)（国立科技大学）；National Center of Artificial Intelligence（国家人工智能中心）；Smart Data and Knowledge Services（智能数据与知识服务部）
Scarcity of large publicly available retinal fundus image datasets for automated glaucoma detection has been the bottleneck for successful application of artificial intelligence towards practical Computer-Aided Diagnosis (CAD). A few small datasets that are available for research community usually suffer from impractical image capturing conditions and stringent inclusion criteria. These shortcomings in already limited choice of existing datasets make it challenging to mature a CAD system so that it can perform in real-world environment. In this paper we present a large publicly available retin…
Catheters are the second most common abnormal finding on radiographs. The position of catheters must be assessed on all radiographs, as serious complications can arise if catheters are malpositioned. However, due to the large number of radiographs performed each day, there can be substantial delays between the time a radiograph is performed and when it is interpreted by a radiologist. Computer-aided approaches hold the potential to assist in prioritizing radiographs with potentially malpositioned catheters for interpretation and automatically insert text indicating the placement of catheters i…
Coronavirus disease (COVID-19) is a contagious infection caused by severe acute respiratory syndrome coronavirus-2 (SARS-COV-2) and it has infected and killed millions of people across the globe. In the absence of specific drugs or vaccines for the treatment of COVID-19 and the limitation of prevailing diagnostic techniques, there is a requirement for some alternate automatic screening systems that can be used by the physicians to quickly identify and isolate the infected patients. A chest X-ray (CXR) image can be used as an alternative modality to detect and diagnose the COVID-19. In this stu…
Social media became popular and percolated almost all aspects of our daily lives. While online posting proves very convenient for individual users, it also fosters fast-spreading of various rumors. The rapid and wide percolation of rumors can cause persistent adverse or detrimental impacts. Therefore, researchers invest great efforts on reducing the negative impacts of rumors. Towards this end, the rumor classification system aims to detect, track, and verify rumors in social media. Such systems typically include four components: (i) a rumor detector, (ii) a rumor tracker, (iii) a stance class…
Massachusetts General Hospital（麻省总医院）；Zhejiang University（浙江大学）；University of Michigan（密歇根大学）；Beijing Institute of Technology（北京理工大学）
Tissue characterization has long been an important component of Computer Aided Diagnosis (CAD) systems for automatic lesion detection and further clinical planning. Motivated by the superior performance of deep learning methods on various computer vision problems, there has been increasing work applying deep learning to medical image analysis. However, the development of a robust and reliable deep learning model for computer-aided diagnosis is still highly challenging due to the combination of the high heterogeneity in the medical images and the relative lack of training samples. Specifically,…
Explainable AI for Multivariate Time Series Pattern Exploration: Latent Space Visual Analytics with Temporal Fusion Transformer and Variational Autoencoders in Power Grid Event Diagnosis
Near-term noisy intermediate-scale quantum circuits can efficiently implement implicit probabilistic models in discrete spaces, supporting distributions that are practically infeasible to sample from using classical means. One of the possible applications of such models, also known as Born machines, is probabilistic inference, which is at the core of Bayesian methods. This paper studies the use of Born machines for the problem of training binary Bayesian neural networks. In the proposed approach, a Born machine is used to model the variational distribution of the binary weights of the neural n…
Artificial Intelligence in healthcare is a new and exciting frontier and the possibilities are endless. With deep learning approaches beating human performances in many areas, the logical next step is to attempt their application in the health space. For these and other Machine Learning approaches to produce good results and have their potential realized, the need for, and importance of, large amounts of accurate data is second to none. This is a challenge faced by many industries and more so in the healthcare space. We present an approach of using Variational Autoencoders (VAE's) as an approa…
Institut de Robòtica i Informàtica Industrial, UPC-CSIC（加泰罗尼亚理工大学-西班牙国家研究委员会机器人与工业信息技术研究所）；Institut Pediàtric de Malalties Rares, Hospital de Sant Joan de Déu（罕见病儿科研究所，圣若安·德乌医院）；Hospital de Sant Joan de Déu（圣若安·德乌医院）
The development of machine learning systems for the diagnosis of rare diseases is challenging mainly due the lack of data to study them. Despite this challenge, this paper proposes a system for the Computer Aided Diagnosis (CAD) of low-prevalence, congenital muscular dystrophies from confocal microscopy images. The proposed CAD system relies on a Convolutional Neural Network (CNN) which performs an independent classification for non-overlapping patches tiling the input image, and generates an overall decision summarizing the individual decisions for the patches on the query image. This decisio…
An integrated approach is proposed across visual and textual data to both determine and justify a medical diagnosis by a neural network. As deep learning techniques improve, interest grows to apply them in medical applications. To enable a transition to workflows in a medical context that are aided by machine learning, the need exists for such algorithms to help justify the obtained outcome so human clinicians can judge their validity. In this work, deep learning methods are used to map a frontal X-Ray image to a continuous textual representation. This textual representation is decoded into a …
In this paper, we analyze the Wisconsin Diagnostic Breast Cancer Data using Machine Learning classification techniques, such as the SVM, Bayesian Logistic Regression (Variational Approximation), and K-Nearest-Neighbors. We describe each model, and compare their performance through different measures. We conclude that SVM has the best performance among all other classifiers, while it competes closely with the Bayesian Logistic Regression that is ranked second best method for this dataset.
Department of Electrical and Computer Engineering, Seoul National University（首尔大学电气与计算机工程系）；Department of Radiology, Seoul National University College of Medicine（首尔大学医学院放射科）；Department of Radiology, Seoul National University Hospital（首尔大学医院放射科）；Institute of Radiation Medicine, Seoul National University Medical Research Center（首尔大学医学研究中心放射医学研究所）；Interdisciplinary Program in Artificial Intelligence, Seoul National University（首尔大学人工智能跨学科项目）
The computer-aided diagnosis of focal liver lesions (FLLs) can help improve workflow and enable correct diagnoses; FLL detection is the first step in such a computer-aided diagnosis. Despite the recent success of deep-learning-based approaches in detecting FLLs, current methods are not sufficiently robust for assessing misaligned multiphase data. By introducing an attention-guided multiphase alignment in feature space, this study presents a fully automated, end-to-end learning framework for detecting FLLs from multiphase computed tomography (CT) images. Our method is robust to misaligned multi…
A Computer-aided Framework for Detecting Osteosarcoma in Computed Tomography Scans
Universidad Carlos III de Madrid, Departamento de Informática, ARCOS, Avenida de la Universidad, 30 (edificio Sabatini), 28911 Leganés (Madrid), España（卡洛斯三世大学马德里分校，信息学院，ARCOS，大学大道30号（萨巴蒂尼大楼），28911 Leganés（马德里），西班牙）；Cinvestav Tamaulipas, Cd. Victoria, Tamaulipas, México（塔毛利帕斯大学，维克多城，塔毛利帕斯，墨西哥）
Enriched text-guided variational multimodal knowledge distillation network (VMD) for automated diagnosis of plaque vulnerability in 3D carotid artery MRI
department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University（放射医学与核医学科，宣武医院，首都医科大学）；Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics（北京磁共振成像与脑信息学重点实验室）；Beijing United Imaging Research Institute of Intelligent Imaging（北京智能成像联合研究院）
Latent Disentanglement in Mesh Variational Autoencoders Improves the Diagnosis of Craniofacial Syndromes and Aids Surgical Planning
Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London（伦敦大学学院Wellcome/EPSRC介入与外科科学中心）；Centre For Medical Image Computing, University College London（伦敦大学学院医学影像计算中心）；UCL Great Ormond Street Institute of Child Health（伦敦大学学院大奥蒙德街儿童健康研究所）；Great Ormond Street Hospital for Children（大奥蒙德街儿童医院）；University of California, Santa Barbara（加州大学圣巴巴拉分校）；Imperial College London（伦敦帝国理工学院）；Erasmus Medical Center（伊拉斯谟医学中心）；Necker - Enfants Malades University Hospital（内克尔-恩凡茨-马拉德大学医院）
Samsung Medical Center（三星医疗中心）；Sungkyunkwan University School of Medicine（成均馆大学医学院）；Sungkyunkwan University（成均馆大学）
Computer-aided diagnosis for low-dose computed tomography (CT) based on deep learning has recently attracted attention as a first-line automatic testing tool because of its high accuracy and low radiation exposure. However, existing methods rely on supervised learning, imposing an additional burden to doctors for collecting disease data or annotating spatial labels for network training, consequently hindering their implementation. We propose a method based on a deep neural network for computer-aided diagnosis called virtual multi-view projection and reconstruction for unsupervised anomaly dete…
University of Edinburgh（爱丁堡大学）；Western General Hospital（西部综合医院）
Deep convolutional neural networks (CNNs) have emerged as a new paradigm for Mammogram diagnosis. Contemporary CNN-based computer-aided-diagnosis (CAD) for breast cancer directly extract latent features from input mammogram image and ignore the importance of morphological features. In this paper, we introduce a novel deep learning framework for mammogram image processing, which computes mass segmentation and simultaneously predict diagnosis results. Specifically, our method is constructed in a dual-path architecture that solves the mapping in a dual-problem manner, with an additional considera…
Computer-aided breast cancer diagnosis in mammography is a challenging problem, stemming from mammographical data scarcity and data entanglement. In particular, data scarcity is attributed to the privacy and expensive annotation. And data entanglement is due to the high similarity between benign and malignant masses, of which manifolds reside in lower dimensional space with very small margin. To address these two challenges, we propose a deep learning framework, named Contrastive Identifier Network (\textsc{COIN}), which integrates adversarial augmentation and manifold-based contrastive learni…
Faculty of Engineering and Applied Science, Ontario Tech University（安大略理工大学工程与应用科学学院）；Faculty of Business and Information Technology, Ontario Tech University（安大略理工大学商业与信息技术学院）
Deep learning has introduced several learning-based methods to recognize breast tumours and presents high applicability in breast cancer diagnostics. It has presented itself as a practical installment in Computer-Aided Diagnostic (CAD) systems to further assist radiologists in diagnostics for different modalities. A deep learning network trained on images provided by hospitals or public databases can perform classification, detection, and segmentation of lesion types. Significant progress has been made in recognizing tumours on 2D images but recognizing 3D images remains a frontier so far. The…
Southern University of Science and Technology（南方科技大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Central South University（中南大学）
The development of noninvasive brain imaging such as resting-state functional magnetic resonance imaging (rs-fMRI) and its combination with AI algorithm provides a promising solution for the early diagnosis of Autism spectrum disorder (ASD). However, the performance of the current ASD classification based on rs-fMRI still needs to be improved. This paper introduces a classification framework to aid ASD diagnosis based on rs-fMRI. In the framework, we proposed a novel filter feature selection method based on the difference between step distribution curves (DSDC) to select remarkable functional …
Research Computing Center at the University of Chicago（芝加哥大学研究计算中心）
Prostate cancer is one of the most common causes of cancer deaths in men. There is a growing demand for noninvasively and accurately diagnostic methods that facilitate the current standard prostate cancer risk assessment in clinical practice. Still, developing computer-aided classification tools in prostate cancer diagnostics from multiparametric magnetic resonance images continues to be a challenge. In this work, we propose a novel deep learning approach for automatic classification of prostate lesions in the corresponding magnetic resonance images by constructing a two-stage multimodal multi…
University of Toronto（多伦多大学）；University of Edinburgh（爱丁堡大学）；Xidian University（西安电子科技大学）
Computer aided diagnosis (CAD) increases diagnosis efficiency, helping doctors providing a quick and confident diagnosis, it has played an important role in the treatment of COVID19. In our task, we solve the problem about abnormality detection and classification. The dataset provided by Kaggle platform and we choose YOLOv5 as our model. We introduce some methods on objective detection in the related work section, the objection detection can be divided into two streams: onestage and two stage. The representational model are Faster RCNN and YOLO series. Then we describe the YOLOv5 model in the …
