Adaptive Contrast for Image Regression in Computer-Aided Disease Assessment
The Hong Kong University of Science and Technology（香港科技大学）；The Hong Kong University（香港大学）；The Hong Kong University of Science and Technology Shenzhen Research Institute（香港科技大学深圳研究院）
Weihang Dai, Xiaomeng Li, Wan Hang Keith Chiu, Michael D. Kuo, Kwang-Ting Cheng
Image regression tasks for medical applications, such as bone mineral density (BMD) estimation and left-ventricular ejection fraction (LVEF) prediction, play an important role in computer-aided disease assessment. Most deep regression methods train the neural network with a single regression loss function like MSE or L1 loss. In this paper, we propose the first contrastive learning framework for deep image regression, namely AdaCon, which consists of a feature learning branch via a novel adaptive-margin contrastive loss and a regression prediction branch. Our method incorporates label distance relationships as part of the learned feature representations, which allows for better performance in downstream regression tasks. Moreover, it can be used as a plug-and-play module to improve performance of existing regression methods. We demonstrate the effectiveness of AdaCon on two medical image regression tasks, ie, bone mineral density estimation from X-ray images and left-ventricular ejection fraction prediction from echocardiogram videos. AdaCon leads to relative improvements of 3.3% and 5.9% in MAE over state-of-the-art BMD estimation and LVEF prediction methods, respectively.
University of New South Wales（新南威尔士大学）；CSIRO（联邦科学与工业研究组织）；Prince of Wales Hospital（威尔士亲王医院）
Cerebral small vessel disease (CSVD) is a major vascular contributor to cognitive impairment in ageing, including dementias. Imaging remains the most promising method for in vivo studies of CSVD. To replace the subjective and laborious visual rating approaches, emerging studies have applied state-of-the-art artificial intelligence to extract imaging biomarkers of CSVD from MRI scans. We aimed to summarise published computer-aided methods to examine three imaging biomarkers of CSVD, namely cerebral microbleeds (CMB), dilated perivascular spaces (PVS), and lacunes of presumed vascular origin. Se…
University of Waterloo（滑铁卢大学）；DarwinAI Corp.（DarwinAI公司）；Stony Brook School of Medicine（石溪大学医学院）；Montefiore Medical Center（蒙蒂菲奥里医疗中心）；Albert Einstein College of Medicine（阿尔伯特·爱因斯坦医学院）
Background: A critical step in effective care and treatment planning for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of the COVID-19 pandemic, is the assessment of the severity of disease progression. Chest x-rays (CXRs) are often used to assess SARS-CoV-2 severity, with two important assessment metrics being extent of lung involvement and degree of opacity. In this proof-of-concept study, we assess the feasibility of computer-aided scoring of CXRs of SARS-CoV-2 lung disease severity using a deep learning system. Materials and Methods: Data consisted of 396 CXRs fro…
German Cancer Research Center (DKFZ)（德国癌症研究中心（DKFZ））；Mannheim University of Applied Science（曼海姆应用科学大学）
Cardiac magnetic resonance imaging improves on diagnosis of cardiovascular diseases by providing images at high spatiotemporal resolution. Manual evaluation of these time-series, however, is expensive and prone to biased and non-reproducible outcomes. In this paper, we present a method that addresses named limitations by integrating segmentation and disease classification into a fully automatic processing pipeline. We use an ensemble of UNet inspired architectures for segmentation of cardiac structures such as the left and right ventricular cavity (LVC, RVC) and the left ventricular myocardium…
Chest X-ray is one of the most accessible medical imaging technique for diagnosis of multiple diseases. With the availability of ChestX-ray14, which is a massive dataset of chest X-ray images and provides annotations for 14 thoracic diseases; it is possible to train Deep Convolutional Neural Networks (DCNN) to build Computer Aided Diagnosis (CAD) systems. In this work, we experiment a set of deep learning models and present a cascaded deep neural network that can diagnose all 14 pathologies better than the baseline and is competitive with other published methods. Our work provides the quantita…
Explainable Cross-Disease Reasoning for Cardiovascular Risk Assessment from Low-Dose Computed Tomography
Department of Computer Science, Emory University（埃默里大学计算机科学系）；Department of Computer Science, Johns Hopkins University（约翰霍普金斯大学计算机科学系）；Department of Radiation Oncology（放射肿瘤学部）；Winship Cancer Institute, Emory University（埃默里大学Winship癌症研究所）
Third Affiliated Hospital of Zhengzhou University, China（郑州大学第三附属医院）；Lehigh University, USA（理海大学）
Objective: Ultrahigh-resolution optical coherence microscopy (OCM) has recently demonstrated its potential for accurate diagnosis of human cervical diseases. One major challenge for clinical adoption, however, is the steep learning curve clinicians need to overcome to interpret OCM images. Developing an intelligent technique for computer-aided diagnosis (CADx) to accurately interpret OCM images will facilitate clinical adoption of the technology and improve patient care. Methods: 497 high-resolution 3-D OCM volumes (600 cross-sectional images each) were collected from 159 ex vivo specimens of …
Lomonosov Moscow State University（莫斯科国立大学）；University of Bordeaux（波尔多大学）；Université Ibn Zhor（伊本·佐尔大学）；University Victor Segalen Bordeaux 2（维克多·瑟加兰-波尔多第二大学）
Computer-aided early diagnosis of Alzheimers Disease (AD) and its prodromal form, Mild Cognitive Impairment (MCI), has been the subject of extensive research in recent years. Some recent studies have shown promising results in the AD and MCI determination using structural and functional Magnetic Resonance Imaging (sMRI, fMRI), Positron Emission Tomography (PET) and Diffusion Tensor Imaging (DTI) modalities. Furthermore, fusion of imaging modalities in a supervised machine learning framework has shown promising direction of research. In this paper we first review major trends in automatic class…
Chronological Contrastive Learning: Few-Shot Progression Assessment in Irreversible Diseases
Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna（维也纳医科大学生物医学成像与图像引导治疗系计算成像研究实验室）；Comprehensive Center for Artificial Intelligence in Medicine, Medical University of Vienna（维也纳医科大学综合医学人工智能中心）；Division of Rheumatology, Department of Medicine III, Medical University of Vienna（维也纳医科大学第三内科风湿病科）；Johnson & Johnson（强生公司）；Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital（麻省总医院Athinoula A. Martinos生物医学成像中心）；Ludwig Boltzmann Institute of Arthritis and Rehabilitation（路德维希·玻尔兹曼关节炎与康复研究所）
Manchester Metropolitan University（曼彻斯特城市大学）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）
Computer-aided early diagnosis of Alzheimer's disease (AD) and its prodromal form mild cognitive impairment (MCI) based on structure Magnetic Resonance Imaging (sMRI) has provided a cost-effective and objective way for early prevention and treatment of disease progression, leading to improved patient care. In this work, we have proposed a novel computer-aided approach for early diagnosis of AD by introducing an explainable 3D Residual Attention Deep Neural Network (3D ResAttNet) for end-to-end learning from sMRI scans. Different from the existing approaches, the novelty of our approach is thre…
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）
Royal College of Art（皇家艺术学院）；University of Warwick（华威大学）
Computer-assisted multimodal training is an effective way of learning complex motor skills in various applications. In particular disciplines (eg. healthcare) incompetency in performing dexterous hands-on examinations (clinical palpation) may result in misdiagnosis of symptoms, serious injuries or even death. Furthermore, a high quality clinical examination can help to exclude significant pathology, and reduce time and cost of diagnosis by eliminating the need for unnecessary medical imaging. Medical palpation is used regularly as an effective preliminary diagnosis method all around the world …
Clinical Priors Guided Lung Disease Detection in 3D CT Scans
College of Computer Science and Artificial Intelligence, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University（计算机科学与人工智能学院，上海智能信息处理重点实验室，复旦大学）；University of Oxford（牛津大学）；The Hong Kong University of Science and Technology（香港科技大学）
Deep learning models have the capacity to fundamentally revolutionize medical imaging analysis, and they have particularly interesting applications in computer-aided diagnosis. We attempt to use deep learning neural networks to diagnose funduscopic images, visual representations of the interior of the eye. Recently, a few robust deep learning approaches have performed binary classification to infer the presence of a specific ocular disease, such as glaucoma or diabetic retinopathy. In an effort to broaden the applications of computer-aided ocular disease diagnosis, we propose a unifying model …
Memorial Sloan Kettering Cancer Center（纪念斯隆凯特琳癌症中心）；Indian Institute of Technology Madras（印度理工学院马德拉斯分校）
Purpose: A core component of advancing cancer treatment research is assessing response to therapy. Doing so by hand, for example as per RECIST or RANO criteria, is tedious, time-consuming, and can miss important tumor response information; most notably, they exclude non-target lesions. We wish to assess change in a holistic fashion that includes all lesions, obtaining simple, informative, and automated assessments of tumor progression or regression. Due to often low patient enrolments in clinical trials, we wish to make response assessments with small training sets. Deep neuroevolution (DNE) c…
COVID-19 Computer-aided Diagnosis through AI-assisted CT Imaging Analysis: Deploying a Medical AI System
University of Ioannina（约阿尼纳大学）；AIandMe SMPC（AIandMe SMPC公司）；National Technical University of Athens（雅典国立技术大学）；Queen Mary University of London（伦敦大学玛丽皇后学院）
A Symmetric Regressor for MRI-Based Assessment of Striatal Dopamine Transporter Uptake in Parkinson's Disease With Enhanced Uncertainty Estimation
Hankuk University of Foreign Studies（韩国外国语大学）；Seoul National University Bundang Hospital（首尔大学盆唐医院）；Seoul National University College of Medicine（首尔大学医学院）
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…
Automated Histopathologic Assessment of Hirschsprung Disease Using a Multi-Stage Vision Transformer Framework
Department of Systems and Computer Engineering, Carleton University（系统与计算机工程系，卡尔顿大学）；Department of Clinical Science and Translational Medicine, University of Ottawa（临床科学与转化医学系，渥太华大学）；School of Epidemiology and Public Health, University of Ottawa（流行病学与公共卫生学院，渥太华大学）；Department of Methodological and Implementation Research, Ottawa Hospital Research Institute（方法学与实施研究系，渥太华医院研究所）；Children’s Hospital of Eastern Ontario (CHEO)（东部 Ontario儿童医院（CHEO））
Ferdowsi University of Mashhad（马什哈德菲尔多西大学）；Mashhad University of Medical Sciences（马什哈德医科大学）
Todays, researchers in the field of Pulmonary Embolism (PE) analysis need to use a publicly available dataset to assess and compare their methods. Different systems have been designed for the detection of pulmonary embolism (PE), but none of them have used any public datasets. All papers have used their own private dataset. In order to fill this gap, we have collected 5160 slices of computed tomography angiography (CTA) images acquired from 20 patients, and after labeling the image by experts in this field, we provided a reliable dataset which is now publicly available. In some situation, PE d…
Crop diseases are a major threat to food security and their rapid identification is important to prevent yield loss. Swift identification of these diseases are difficult due to the lack of necessary infrastructure. Recent advances in computer vision and increasing penetration of smartphones have paved the way for smartphone-assisted disease identification. Most of the plant diseases leave particular artifacts on the foliar structure of the plant. This study was conducted in 2020 at Department of Computer Science and Engineering, University of Engineering and Technology, Lahore, Pakistan to che…
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…
The University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校）；The University of Massachusetts Medical School（马萨诸塞大学医学院）
Worldwide, in 2014, more than 1.9 billion adults, 18 years and older, were overweight. Of these, over 600 million were obese. Accurately documenting dietary caloric intake is crucial to manage weight loss, but also presents challenges because most of the current methods for dietary assessment must rely on memory to recall foods eaten. The ultimate goal of our research is to develop computer-aided technical solutions to enhance and improve the accuracy of current measurements of dietary intake. Our proposed system in this paper aims to improve the accuracy of dietary assessment by analyzing the…
A Low-Cost UAV Deep Learning Pipeline for Integrated Apple Disease Diagnosis,Freshness Assessment, and Fruit Detection
Department of Electronics and Communication Engineering, Heritage Institute of Technology（电子与通信工程系，遗产理工学院）；Department of Computer Science and Engineering (AI & ML), Heritage Institute of Technology（计算机科学与工程系（人工智能与机器学习），遗产理工学院）
University of Waterloo（滑铁卢大学）；Ludwig-Maximilians University（路德维希-马克西米利安大学）；Technical University of Munich（慕尼黑工业大学）；Schön Klinik München Schwabing（慕尼黑施瓦宾 Schön 诊所）
Parkinson's Disease (PD) is characterized by disorders in motor function such as freezing of gait, rest tremor, rigidity, and slowed and hyposcaled movements. Medication with dopaminergic medication may alleviate those motor symptoms, however, side-effects may include uncontrolled movements, known as dyskinesia. In this paper, an automatic PD motor-state assessment in free-living conditions is proposed using an accelerometer in a wrist-worn wearable sensor. In particular, an ensemble of convolutional neural networks (CNNs) is applied to capture the large variability of daily-living activities …
CORA: Generalizable coronary artery disease assessment and risk stratification from coronary CT angiography using pathology-centric representation learning
Department of Radiology, Northwestern University（西北大学放射学系）；Department of Biomedical Engineering, Northwestern University（西北大学生物医学工程系）；Department of Cardiology, Northwestern University（西北大学心脏病学系）；Department of Preventive Medicine, Northwestern University（西北大学预防医学系）
Research and Academic Computer Network (NASK)（研究与学术计算机网络（NASK））；Warsaw University of Technology（华沙理工大学）；Medical University of Warsaw（华沙医科大学）
This paper presents a database of iris images collected from disease affected eyes and an analysis related to the influence of ocular diseases on iris recognition reliability. For that purpose we have collected a database of iris images acquired for 91 different eyes during routine ophthalmology visits. This collection gathers samples for healthy eyes as well as those with various eye pathologies, including cataract, acute glaucoma, posterior and anterior synechiae, retinal detachment, rubeosis iridis, corneal vascularization, corneal grafting, iris damage and atrophy and corneal ulcers, haze …
Whole-body Representation Learning For Competing Preclinical Disease Risk Assessment
Institute of Diagnostic；Interventional Radiology, Technical University of Munich, School of Medicine, Munich, Germany Chair for AI in Healthcare；Medicine, Technical University of Munich (TUM)；TUM University Hospital, Munich, Germany Department of Computing, Imperial College London, London, UK German Cancer Consortium (DKTK), Munich partner site, Heidelberg, Germany
Department of Computer Science & Engineering, Priyadarshini Institute of Engineering & Technology（普里达尔希尼工程技术学院计算机科学与工程系）
Diabetic Retinopathy is a critical health problem influences 100 million individuals worldwide, and these figures are expected to rise, particularly in Asia. Diabetic Retinopathy is a chronic eye disease which can lead to irreversible vision loss. Considering the visual complexity of retinal images, the early-stage diagnosis of Diabetic Retinopathy can be challenging for human experts. However, Early detection of Diabetic Retinopathy can significantly help to avoid permanent vision loss. The capability of computer-aided detection systems to accurately and efficiently detect the diabetic retino…
Toronto Rehabilitation Institute – University Health Network（多伦多康复研究所 - 大学健康网络）；Massachusetts Eye and Ear Infirmary（麻省眼耳医院）；Harvard Medical School（哈佛医学院）；University of Toronto（多伦多大学）；Sunnybrook Research Institute（桑尼布鲁克研究所）；Royal Australasian College of Surgeons（澳大利亚皇家外科医师学院）；University of Sydney（悉尼大学）；Brasilia University（巴西利亚大学）
Importance: Machine learning (ML) approaches to facial landmark localization carry great clinical potential for quantitative assessment of facial function as they enable high-throughput automated quantification of relevant facial metrics from photographs. However, translation from research settings to clinical applications requires important improvements. Objective: To develop an ML algorithm for accurate facial landmarks localization in photographs of facial palsy patients, and use it as part of an automated computer-aided diagnosis system. Design, Setting, and Participants: Facial landmarks …
GastroVision: A Multi-class Endoscopy Image Dataset for Computer Aided Gastrointestinal Disease Detection
Northwestern University（西北大学）；Indian Institute of Technology Guwahati（印度理工学院古瓦哈提分校）；Jefferson Health NJ（新泽西杰斐逊医疗）；SimulaMet（西穆拉梅特研究所）；Sahlgrenska University Hospital（萨尔格伦斯卡大学医院）
CNRS（法国国家科学研究中心）；University of Bordeaux 1（波尔多第一大学）；INSERM（法国国家健康与医学研究院）
Egocentric vision technology consists in capturing the actions of persons from their own visual point of view using wearable camera sensors. We apply this new paradigm to instrumental activities monitoring with the objective of providing new tools for the clinical evaluation of the impact of the disease on persons with dementia. In this paper, we introduce the current state of the development of this technology and focus on two technology modules: automatic location estimation and visual saliency estimation for content interpretation.
Early Diagnosis and Severity Assessment of Weligama Coconut Leaf Wilt Disease and Coconut Caterpillar Infestation using Deep Learning-based Image Processing Techniques
Sri Lanka Institute of Information Technology（斯里兰卡信息技术学院）；Victorian Institute of Technology（维多利亚理工学院）；Keio University（庆应义塾大学）；ACSL Ltd（ACSL有限公司）
State Key Laboratory for Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）；National Institute of Healthcare Data Science, Nanjing University（南京大学国家健康医疗大数据研究院）；School of Computer Science, Nanjing Normal University（南京师范大学计算机科学与技术学院）；Department of Radiology and BRIC, UNC Chapel Hill（北卡罗来纳大学教堂山分校放射学系与生物医学研究成像中心）；Inception Institute of Artificial Intelligence（起源人工智能研究院）；Nanjing Medical University（南京医科大学）；Nanjing Brain Hospital（南京脑科医院）；Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能医疗科技有限公司研发部）；Department of Brain and Cognitive Engineering, Korea University（高丽大学脑与认知工程系）
In this paper, we study a novel problem: "automatic prescription recommendation for PD patients." To realize this goal, we first build a dataset by collecting 1) symptoms of PD patients, and 2) their prescription drug provided by neurologists. Then, we build a novel computer-aided prescription model by learning the relation between observed symptoms and prescription drug. Finally, for the new coming patients, we could recommend (predict) suitable prescription drug on their observed symptoms by our prescription model. From the methodology part, our proposed model, namely Prescription viA Learni…
Cross-Corpus and Cross-domain Handwriting Assessment of NeuroDegenerative Diseases via Time-Series-to-Image Conversion
Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore MD, USA（电气与计算机工程系，约翰霍普金斯大学，巴尔的摩 MD，美国）；Department of Psychiatry and Behavioral Sciences, The Johns Hopkins University, Baltimore MD, USA（精神病学与行为科学系，约翰霍普金斯大学，巴尔的摩 MD，美国）；Department of Neurology, The Johns Hopkins University, Baltimore MD, USA（神经病学系，约翰霍普金斯大学，巴尔的摩 MD，美国）
Breast cancer is the most frequently diagnosed cancer and leading cause of cancer-related death among females worldwide. In this article, we investigate the applicability of densely connected convolutional neural networks to the problems of histology image classification and whole slide image segmentation in the area of computer-aided diagnoses for breast cancer. To this end, we study various approaches for transfer learning and apply them to the data set from the 2018 grand challenge on breast cancer histology images (BACH).
Large Language Model Aided Birt-Hogg-Dube Syndrome Diagnosis with Multimodal Retrieval-Augmented Generation
School of Computer Science and Technology（计算机科学与技术学院）；Department of Pulmonary and Critical Care Medicine（呼吸与危重症医学科）；Center for Diagnosis and Management of Rare Diseases（罕见病诊断与管理中心）；the First Affiliated Hospital of USTC（USTC第一附属医院）；Division of Life Sciences and Medicine（生命科学与医学系）；USTC；WanNan Medical College（皖南医学院）
IBM Research Australia（IBM研究院澳大利亚分院）；IBM Research Yorktown Heights（IBM研究院约克镇高地分院）
This paper presents a novel deep learning enabled, video based analysis framework for assessing the Unified Parkinsons Disease Rating Scale (UPDRS) that can be used in the clinic or at home. We report results from comparing the performance of the framework to that of trained clinicians on a population of 32 Parkinsons disease (PD) patients. In-person clinical assessments by trained neurologists are used as the ground truth for training our framework and for comparing the performance. We find that the standard sit-to-stand activity can be used to evaluate the UPDRS sub-scores of bradykinesia (B…
Research and Academic Computer Network（研究与学术计算机网络）；Warsaw University of Technology（华沙理工大学）；Medical University of Warsaw（华沙医科大学）
This paper presents an analysis of how the iris recognition is impacted by eye diseases and an appropriate dataset comprising 2996 iris images of 230 distinct eyes (including 184 illness-affected eyes representing more than 20 different eye conditions). The images were collected in near infrared and visible light during a routine ophthalmological practice. The experimental study shows four valuable results. First, the enrollment process is highly sensitive to those eye conditions that make the iris obstructed or introduce geometrical distortions. Second, even those conditions that do not produ…
CARE-PD: A Multi-Site Anonymized Clinical Dataset for Parkinson's Disease Gait Assessment
University of Toronto（多伦多大学）；Vector Institute（向量研究所）；KITE Research Institute-UHN（KITE研究 institute-UHN）；University of Strasbourg（斯特拉斯堡大学）；University Hospitals of Strasbourg（斯特拉斯堡大学医院）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Federal University of ABC（巴西ABC联邦大学）；KU Leuven（鲁文大学）；Hasselt University（哈瑟尔特大学）；Emory University（埃默里大学）；University of Bristol（布里斯托尔大学）
KITE — Toronto Rehabilitation Institute — University Health Network（凯特大西洋康复研究所——多伦多康复研究所——大学医疗网络）；University of Toronto（多伦多大学）；Sunnybrook Research Institute（森尼布鲁克研究所）
Orofacial deficits are common in people with Parkinson's disease (PD) and their evolution might represent an important biomarker of disease progression. We are developing an automated system for assessment of orofacial function in PD that can be used in-home or in-clinic and can provide useful and objective clinical information that informs disease management. Our current approach relies on color and depth cameras for the estimation of 3D facial movements. However, depth cameras are not commonly available, might be expensive, and require specialized software for control and data processing. Th…
The Third Affiliated Hospital of Tsinghua University（清华大学附属北京清华长庚医院）；Zhengzhou University（郑州大学）
As a disease with malignant potential, hydatidiform mole (HM) is one of the most common gestational trophoblastic diseases. For pathologists, the HM section of hydrops lesions is an important basis for diagnosis. In pathology departments, the diverse microscopic manifestations of HM lesions and the limited view under the microscope mean that physicians with extensive diagnostic experience are required to prevent missed diagnosis and misdiagnosis. Feature extraction can significantly improve the accuracy and speed of the diagnostic process. As a remarkable diagnosis assisting technology, comput…
University of Bristol（布里斯托大学）；Southmead Hospital（南米德医院）
Evaluating neurological disorders such as Parkinson's disease (PD) is a challenging task that requires the assessment of several motor and non-motor functions. In this paper, we present an end-to-end deep learning framework to measure PD severity in two important components, hand movement and gait, of the Unified Parkinson's Disease Rating Scale (UPDRS). Our method leverages on an Inflated 3D CNN trained by a temporal segment framework to learn spatial and long temporal structure in video data. We also deploy a temporal attention mechanism to boost the performance of our model. Further, motion…
CardiacNet: Learning to Reconstruct Abnormalities for Cardiac Disease Assessment from Echocardiogram Videos
The Hong Kong University of Science and Technology（香港科技大学）；Guangdong Provincial People’s Hospital（广东省人民医院）；HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute（香港科技大学深港协同创新研究院）
College of Science, Liaoning Petrochemical University（辽宁石油化工大学理学院）；Industrial Mathematics Laboratory, Baikal School of BRICS, Irkutsk National Research Technical University（伊尔库茨克国立研究技术大学贝加尔金砖学院工业数学实验室）
Biodegradable elastic scaffolds have attracted more and more attention in the field of soft tissue repair and tissue engineering. These scaffolds made of porous bioelastomers support tissue ingrowth along with their own degradation. It is necessary to develop a computer-aided analyzing method based on ultrasound images to identify the degradation performance of the scaffold, not only to obviate the need to do destructive testing, but also to monitor the scaffold's degradation and tissue ingrowth over time. It is difficult using a single traditional image processing algorithm to extract continu…
Brno University of Technology（布尔诺理工大学）；Tecnocampus（特克诺坎普斯）；Masaryk University（马萨里克大学）
To this date, studies focusing on the prodromal diagnosis of Lewy body diseases (LBDs) based on quantitative analysis of graphomotor and handwriting difficulties are missing. In this work, we enrolled 18 subjects diagnosed with possible or probable mild cognitive impairment with Lewy bodies (MCI-LB), 7 subjects having more than 50% probability of developing Parkinson's disease (PD), 21 subjects with both possible/probable MCI-LB and probability of PD > 50%, and 37 age- and gender-matched healthy controls (HC). Each participant performed three tasks: Archimedean spiral drawing (to quantify grap…
3D image scans are an assessment tool for neurological damage in Parkinson's disease (PD) patients. This diagnosis process can be automatized to help medical staff through Decision Support Systems (DSSs), and Convolutional Neural Networks (CNNs) are good candidates, because they are effective when applied to spatial data. This paper proposes a 3D CNN ordinal model for assessing the level or neurological damage in PD patients. Given that CNNs need large datasets to achieve acceptable performance, a data augmentation method is adapted to work with spatial data. We consider the Ordinal Graph-base…
Higher-Order Domain Generalization in Magnetic Resonance-Based Assessment of Alzheimer's Disease
Faculty of Engineering and Natural Sciences (VPA Lab), Sabanci University, Istanbul, Türkiye（工程与自然科学学院（VPA实验室），萨班奇大学，伊斯坦布尔，土耳其）；Department of Computer Science, Boston University（计算机科学系，波士顿大学）；Department of Medicine, Boston University Chobanian & Avedisian School of Medicine（医学系，波士顿大学乔班尼安与阿维迪西安医学院；计算机科学系和计算与数据科学学院，波士顿大学）；Department of Computer Science and Faculty of Computing and Data Sciences, Boston University
University College London（伦敦大学学院）；UCL Institute of Cardiovascular Science（伦敦大学学院心血管科学研究所）；Great Ormond Street Hospital for Children（大奥蒙德街儿童医院）
One of the main sources of error in multi-atlas segmentation propagation approaches comes from the use of atlas databases that are morphologically dissimilar to the target image. In this work, we exploit the segmentation errors associated with poor atlas selection to build a computer aided diagnosis (CAD) system for pathological classification in post-operative dextro-transposition of the great arteries (d-TGA). The proposed approach extracts a set of features, which describe the quality of a segmentation, and introduces them into a logical decision tree that provides the final diagnosis. We h…
The health and socioeconomic difficulties caused by the COVID-19 pandemic continues to cause enormous tensions around the world. In particular, this extraordinary surge in the number of cases has put considerable strain on health care systems around the world. A critical step in the treatment and management of COVID-19 positive patients is severity assessment, which is challenging even for expert radiologists given the subtleties at different stages of lung disease severity. Motivated by this challenge, we introduce COVID-Net CT-S, a suite of deep convolutional neural networks for predicting l…
University of Virginia（弗吉尼亚大学）；Columbia University（哥伦比亚大学）
Eosinophilic Esophagitis (EoE) is an inflammatory esophageal disease which is increasing in prevalence. The diagnostic gold-standard involves manual review of a patient's biopsy tissue sample by a clinical pathologist for the presence of 15 or greater eosinophils within a single high-power field (400x magnification). Diagnosing EoE can be a cumbersome process with added difficulty for assessing the severity and progression of disease. We propose an automated approach for quantifying eosinophils using deep image segmentation. A U-Net model and post-processing system are applied to generate eosi…
SAND: The Challenge on Speech Analysis for Neurodegenerative Disease Assessment
National Research Council of Italy (CNR), Institute for High-Performance Computing and Networking (ICAR), Naples（意大利国家研究理事会（CNR）、高性能计算与网络研究所（ICAR）、那不勒斯）
