Preprint version before submission for publication. complete version published in proc. 15th International Conference on Frontiers of Information Technology (FIT 2017), December 18-20, 2017, Islamabad, Pakistan. http://ieeexplore.ieee.org/document/8261064/
发表信息：
15th IEEE International Conference on Frontiers of Information Technology (FIT 2017), December 18-20, 2017, Islamabad, Pakistan
This paper presents a novel approach for detection of liver abnormalities in an automated manner using ultrasound images. For this purpose, we have implemented a machine learning model that can not only generate labels (normal and abnormal) for a given ultrasound image but it can also detect when its prediction is likely to be incorrect. The proposed model abstains from generating the label of a test example if it is not confident about its prediction. Such behavior is commonly practiced by medical doctors who, when given insufficient information or a difficult case, can chose to carry out further clinical or diagnostic tests before generating a diagnosis. However, existing machine learning models are designed in a way to always generate a label for a given example even when the confidence of their prediction is low. We have proposed a novel stochastic gradient based solver for the learning with abstention paradigm and use it to make a practical, state of the art method for liver disease classification. The proposed method has been benchmarked on a data set of approximately 100 patients from MINAR, Multan, Pakistan and our results show that the proposed scheme offers state of the art classification performance.
A Multi-Stage Deep Learning Framework with PKCP-MixUp Augmentation for Pediatric Liver Tumor Diagnosis Using Multi-Phase Contrast-Enhanced CT
Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science and Technology（华中科技大学同济医学院武汉儿童医院（武汉市妇幼保健院））；Wuhan University（武汉大学）；Wuhan Cloud Computing Technology Co., Ltd.（武汉云计算技术有限公司）
A Multimodal Deep Learning Framework for Early Diagnosis of Liver Cancer via Optimized BiLSTM-AM-VMD Architecture
Department of Encephalopathy, Chengdu Pidu District Hospital of Traditional Chinese Medicine, Chengdu 611730, China（成都Pidu区中医医院神经病科）；Department of Tuina, Chengdu Pidu District Hospital of Traditional Chinese Medicine, Chengdu 611730, China（成都Pidu区中医医院推拿科）；Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA（卡内基梅隆大学电气与计算机工程系）
Fetal growth restriction (FGR) is a prevalent pregnancy condition characterised by failure of the fetus to reach its genetically predetermined growth potential. We explore the application of model fitting techniques, linear regression machine learning models, deep learning regression, and Haralick textured features from multi-contrast MRI for multi-fetal organ analysis of FGR. We employed T2 relaxometry and diffusion-weighted MRI datasets (using a combined T2-diffusion scan) for 12 normally grown and 12 FGR gestational age (GA) matched pregnancies. We applied the Intravoxel Incoherent Motion M…
Application of artificial intelligence in medicine brings in highly accurate predictions achieved by complex models, the reasoning of which is hard to interpret. Their generalization ability can be reduced because of the lack of pixel wise annotated images that occurs in frozen section tissue analysis. To partially overcome this gap, this paper explores the approximate explicit feature map (aEFM) transform of low-dimensional data into a low-dimensional subspace in Hilbert space. There, with a modest increase in computational complexity, linear algorithms yield improved performance and keep int…
Accurate segmentation of the liver is a prerequisite for the diagnosis of disease. Automated segmentation is an important application of computer-aided detection and diagnosis of liver disease. In recent years, automated processing of medical images has gained breakthroughs. However, the low contrast of abdominal scan CT images and the complexity of liver morphology make accurate automatic segmentation challenging. In this paper, we propose RA V-Net, which is an improved medical image automatic segmentation model based on U-Net. It has the following three main innovations. CofRes Module (Compo…
Image Sciences Institute, University Medical Center Utrecht（乌得勒支大学医学中心图像科学研究所）；Department of Biomedical Engineering, Eindhoven University of Technology（埃因霍温理工大学生物医学工程系）
Despite the successes of deep learning techniques at detecting objects in medical images, false positive detections occur which may hinder an accurate diagnosis. We propose a technique to reduce false positive detections made by a neural network using an SVM classifier trained with features derived from the uncertainty map of the neural network prediction. We demonstrate the effectiveness of this method for the detection of liver lesions on a dataset of abdominal MR images. We find that the use of a dropout rate of 0.5 produces the least number of false positives in the neural network predicti…
Hunan University of Technology and Business（湖南工商大学）；Utah State University（犹他州立大学）；University of Idaho（爱达荷大学）
Liver segmentation from abdominal CT images is an essential step for liver cancer computer-aided diagnosis and surgical planning. However, both the accuracy and robustness of existing liver segmentation methods cannot meet the requirements of clinical applications. In particular, for the common clinical cases where the liver tissue contains major pathology, current segmentation methods show poor performance. In this paper, we propose a novel low-rank tensor decomposition (LRTD) based multi-atlas segmentation (MAS) framework that achieves accurate and robust pathological liver segmentation of C…
China University of Petroleum（中国石油大学）；Qingdao Municipal Hospital（青岛市市立医院）；University of Chinese Academy of Sciences（中国科学院大学）
Liver cancer is one of the most common malignant diseases in the world. Segmentation and labeling of liver tumors and blood vessels in CT images can provide convenience for doctors in liver tumor diagnosis and surgical intervention. In the past decades, many state-of-the-art medical image segmentation algorithms appeared during this period. With the development of embedded devices, embedded deployment for medical segmentation and automatic reconstruction brings prospects for future automated surgical tasks. Yet, most of the existing segmentation methods mostly care about the spatial feature co…
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Shandong University（山东大学）；Beijing University of Chinese Medicine（北京中医药大学）
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…
Tel Aviv University（特拉维夫大学）；The Chaim Sheba Medical Center（夏姆谢巴医疗中心）；Bar-Ilan University（巴伊兰大学）
Deep learning methods, and in particular convolutional neural networks (CNNs), have led to an enormous breakthrough in a wide range of computer vision tasks, primarily by using large-scale annotated datasets. However, obtaining such datasets in the medical domain remains a challenge. In this paper, we present methods for generating synthetic medical images using recently presented deep learning Generative Adversarial Networks (GANs). Furthermore, we show that generated medical images can be used for synthetic data augmentation, and improve the performance of CNN for medical image classificatio…
Otto von Guericke University（奥托·冯·格里克大学）；German Center for Neurodegenerative Disease（德国神经退行性疾病中心）；Center for Behavioral Brain Sciences（行为脑科学中心）；Leibniz Institute for Neurobiology（莱布尼茨神经生物学研究所）
Segmentation of biomedical images can assist radiologists to make a better diagnosis and take decisions faster by helping in the detection of abnormalities, such as tumors. Manual or semi-automated segmentation, however, can be a time-consuming task. Most deep learning based automated segmentation methods are supervised and rely on manually segmented ground-truth. A possible solution for the problem would be an unsupervised deep learning based approach for automated segmentation, which this research work tries to address. We use a W-Net architecture and modified it, such that it can be applied…
Ankara University（安卡拉大学）；Turkish Aerospace（土耳其航空航天工业公司）；Akgun R&D Center（Akgun研发中心）
Medical imaging has been employed to support medical diagnosis and treatment. It may also provide crucial information to surgeons to facilitate optimal surgical preplanning and perioperative management. Essentially, semi-automatic organ and tumor segmentation has been studied by many researchers. Recently, with the development of Deep Learning (DL) algorithms, automatic organ segmentation has been gathered lots of attention from the researchers. This paper addresses to propose the most efficient DL architectures for Liver segmentation by adapting and comparing state-of-the-art DL frameworks, s…
Diagnosis of liver infection at preliminary stage is important for better treatment. In todays scenario devices like sensors are used for detection of infections. Accurate classification techniques are required for automatic identification of disease samples. In this context, this study utilizes data mining approaches for classification of liver patients from healthy individuals. Four algorithms (Naive Bayes, Bagging, Random forest and SVM) were implemented for classification using R platform. Further to improve the accuracy of classification a hybrid NeuroSVM model was developed using SVM and…
AI-Driven Non-Invasive Detection and Staging of Steatosis in Fatty Liver Disease Using a Novel Cascade Model and Information Fusion Techniques
University of Tehran（德黑兰大学）；York University（约克大学）；Northeastern University（东北大学）；Antalya Bilim University（安塔利亚比尔姆大学）；Research Institute for Gastroenterology and Liver Diseases（胃肠病学和肝病学研究所）；Shahid Beheshti University of Medical Sciences（沙希德贝赫什提医科大学）；University of Waterloo（滑铁卢大学）
LiFUSE: Frequency-Spatial Unified Self-Supervised Pretraining for Liver Disease Analysis on Non-contrast CT
College of Biomedical Engineering, Fudan University（复旦大学生物医学工程学院）；DAMO Academy, Alibaba Group（阿里巴巴集团达摩院）；Hupan Lab（湖畔实验室）；First Affiliated Hospital of Zhejiang University（浙江大学附属第一医院）；Shengjing Hospital of China Medical University（中国医科大学附属盛京医院）
Texas Tech University（德克萨斯理工大学）；The University of Texas at El Paso（德克萨斯大学埃尔帕索分校）
For a medical diagnosis, health professionals use different kinds of pathological ways to make a decision for medical reports in terms of patients medical condition. In the modern era, because of the advantage of computers and technologies, one can collect data and visualize many hidden outcomes from them. Statistical machine learning algorithms based on specific problems can assist one to make decisions. Machine learning data driven algorithms can be used to validate existing methods and help researchers to suggest potential new decisions. In this paper, multiple imputation by chained equatio…
Objective : Abdominal anatomy segmentation is crucial for numerous applications from computer-assisted diagnosis to image-guided surgery. In this context, we address fully-automated multi-organ segmentation from abdominal CT and MR images using deep learning. Methods: The proposed model extends standard conditional generative adversarial networks. Additionally to the discriminator which enforces the model to create realistic organ delineations, it embeds cascaded partially pre-trained convolutional encoder-decoders as generator. Encoder fine-tuning from a large amount of non-medical images all…
StackLiverNet: A Novel Stacked Ensemble Model for Accurate and Interpretable Liver Disease Detection
East West University（东西大学）；Bangladesh Agricultural University（孟加拉国农业大学）；Daffodil International University（达福迪尔国际大学）；University of Dhaka（达卡大学）；University of Barishal（巴里萨尔大学）；Cumilla Victoria Government College（库米拉维多利亚政府学院）；Bangladesh University of Business and Technology（孟加拉国商业与技术大学）
Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images
Visvesvaraya National Institute of Technology（维斯瓦拉亚国家理工学院）；AIIMS Nagpur（那格浦尔全印度医学科学研究所）；Indian Institute of Technology Mandi（曼迪印度理工学院）；Universitat de Barcelona（巴塞罗那大学）
Applied Science Private University（应用科学私立大学）；Jadara University（贾达拉大学）；Princess Sumaya University for Technology（苏玛雅公主科技大学）；King Hussein Medical Center（侯赛因国王医疗中心）
The biliary tree is a network of tubes that connects the liver to the gallbladder, an organ right beneath it. The bile duct is the major tube in the biliary tree. The dilatation of a bile duct is a key indicator for more major problems in the human body, such as stones and tumors, which are frequently caused by the pancreas or the papilla of vater. The detection of bile duct dilatation can be challenging for beginner or untrained medical personnel in many circumstances. Even professionals are unable to detect bile duct dilatation with the naked eye. This research presents a unique vision-based…
University of Alberta（阿尔伯塔大学）；Alberta Machine Intelligence Institute（阿尔伯塔机器智能研究所）；National University of Singapore（新加坡国立大学）
Deep learning approaches often require huge datasets to achieve good generalization. This complicates its use in tasks like image-based medical diagnosis, where the small training datasets are usually insufficient to learn appropriate data representations. For such sensitive tasks it is also important to provide the confidence in the predictions. Here, we propose a way to learn and use probabilistic labels to train accurate and calibrated deep networks from relatively small datasets. We observe gains of up to 22% in the accuracy of models trained with these labels, as compared with traditional…
Machine Learning Driven Biomarker Selection for Medical Diagnosis
Fulton Schools of Engineering, Arizona State University（亚利桑那州立大学富尔顿工程学院）；Biodesign Center for Personalized Diagnostics, Arizona State University（亚利桑那州立大学生物设计个性化诊断中心）；College of Health Solutions, Arizona State University（亚利桑那州立大学健康解决方案学院）
Does Machine Learning Outperform Traditional Fibrosis Scores in Predicting Liver Cirrhosis Risk? A Longitudinal EHR-Based Study
Center for Health Systems Innovation, Oklahoma State University（俄克拉荷马州立大学健康系统创新中心）；Department of Management Science and Information Systems, Oklahoma State University（俄克拉荷马州立大学管理科学与信息系统系）；Department of Health Services Administration, University of Alabama at Birmingham（阿拉巴马大学伯明翰分校健康服务管理系）；Division of Gastroenterology and Hepatology, Department of Medicine, University of Alabama at Birmingham（阿拉巴马大学伯明翰分校消化内科与肝病科）；College of Medicine, The University of Oklahoma Health Campus（俄克拉荷马大学健康校园医学院）；Department of Family and Community Medicine, University of Alabama at Birmingham（阿拉巴马大学伯明翰分校家庭与社区医学系）
University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；California NanoSystems Institute（加州纳米系统研究所）；David Geffen School of Medicine（戴维·格芬医学院）
Histological analysis of tissue samples is one of the most widely used methods for disease diagnosis. After taking a sample from a patient, it goes through a lengthy and laborious preparation, which stains the tissue to visualize different histological features under a microscope. Here, we demonstrate a label-free approach to create a virtually-stained microscopic image using a single wide-field auto-fluorescence image of an unlabeled tissue sample, bypassing the standard histochemical staining process, saving time and cost. This method is based on deep learning, and uses a convolutional neura…
