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A Tetrahedron-Based Heat Flux Signature for Cortical Thickness Morphometry Analysis [chapter]

Yonghui Fan, Gang Wang, Natasha Lepore, Yalin Wang
2018 Lecture Notes in Computer Science  
for Histopathology 351 Combining Convolutional and Recurrent Neural Networks for Classification of Focal Liver Lesions in Multi-Phase CT Images 352 Domain and Geometry Agnostic CNNs for Left Atrium Segmentation  ...  Generative Adversarial Networks for Metal Artifact Reduction in CT Images of the Head 673 Deep Recurrent Level Set for Segmenting Brain Tumors 676 Deep convolutional filtering for spatio-temporal denoising  ... 
doi:10.1007/978-3-030-00931-1_48 pmid:30338317 pmcid:PMC6191198 fatcat:dqhvpm5xzrdqhglrfftig3qejq

Dual-Attention Enhanced BDense-UNet for Liver Lesion Segmentation [article]

Wenming Cao, Philip L.H. Yu, Gilbert C.S. Lui, Keith W.H. Chiu, Ho-Ming Cheng, Yanwen Fang, Man-Fung Yuen, Wai-Kay Seto
2021 arXiv   pre-print
We conduct experiments on liver CT image data sets collected from multiple hospitals by comparing them with state-of-the-art segmentation models.  ...  weights for these two kinds of features.  ...  [8] classify multi-phase CT images of focal liver lesions by combining convolutional networks and recurrent networks. Ouhmichi et al.  ... 
arXiv:2107.11645v1 fatcat:7edlrdpdxzbs5k6nxd2kaug2oi

Radiomics and Deep Learning: Hepatic Applications

Hyo Jung Park, Bumwoo Park, Seung Soo Lee
2020 Korean Journal of Radiology  
Radiomics and deep learning have recently gained attention in the imaging assessment of various liver diseases.  ...  Recent research has demonstrated the potential utility of radiomics and deep learning in staging liver fibroses, detecting portal hypertension, characterizing focal hepatic lesions, prognosticating malignant  ...  Convolutional Neural Network Convolutional neural network (CNN) is the most popular type of deep learning architecture in medical imaging analysis (41, 42) .  ... 
doi:10.3348/kjr.2019.0752 pmid:32193887 pmcid:PMC7082656 fatcat:kncq2om26rg5hf4quusf6z5wk4

State of the Art in Artificial Intelligence and Radiomics in Hepatocellular Carcinoma

Anna Castaldo, Davide Raffaele De Lucia, Giuseppe Pontillo, Marco Gatti, Sirio Cocozza, Lorenzo Ugga, Renato Cuocolo
2021 Diagnostics  
While imaging characteristics of HCC are well-defined in the diagnostic phase, challenging cases still occur, and current prognostic and predictive models are limited in their accuracy.  ...  Often HCC develops in a chronic liver disease setting, and early diagnosis as well as accurate screening of high-risk patients is crucial for appropriate and effective management of these patients.  ...  ; PV: portal venous; AUC: area under the curve; DP: diagnostic performance; ANN: artificial neural network. * Results respectively for focal liver lesion detection and focal liver lesion characterization  ... 
doi:10.3390/diagnostics11071194 fatcat:zmwt7urwinac7bshxcxm6q7doe

Artificial intelligence in the detection, characterisation and prediction of hepatocellular carcinoma: a narrative review

Michal Kawka, Aleksander Dawidziuk, Long R. Jiao, Tamara M. H. Gall
2021 Translational Gastroenterology and Hepatology  
and specificity of AI, and implementation of convoluted neural networks.  ...  These AI algorithms based on a combination of clinical data and imaging-extracted features can also support clinical decision making, especially treatment choice.  ...  (convolutional neural network, CNN) combined with Boolean operators.  ... 
doi:10.21037/tgh-20-242 fatcat:csuy7bzr3nelxduakk4znm4cd4

Conditional Generative Adversarial Networks for Metal Artifact Reduction in CT Images of the Ear [chapter]

Jianing Wang, Yiyuan Zhao, Jack H. Noble, Benoit M. Dawant
2018 Lecture Notes in Computer Science  
Classification of Focal Liver Lesions in Multi-Phase CT Images Liang Dong; Lanfen Lin*; Hongjie Hu; Qiaowei Zhang; Qingqing Chen; Yutaro Iwamoto; Xian-Hua Han; Yen-Wei Chen T-41 Construction of a  ...  Renal Cell Carcinoma using Multiple Instance Decisions Aggregated CNN Mohammad Arafat Hussain*; Ghassan Hamarneh; Rafeef Abugharbieh T-40 Combining Convolutional and Recurrent Neural Networks for  ...  T-129 Learning Myelin Content in Multiple Sclerosis from Multimodal MRI through Adversarial Training Wen  ... 
doi:10.1007/978-3-030-00928-1_1 fatcat:ypoj3zplm5awljf6u5c2spgiea

Survey on Recent Works in Computed Tomography based Computer ‑ Aided Diagnosis of Liver using Deep Learning Techniques

E. Emerson Nithiyaraj, S. Arivazhagan
2020 International Journal of Innovative Science and Research Technology  
In this review, the basics of deep learning is introduced and their success in liver segmentation and lesion detection, classification using CT imaging modality is reviewed and their different network  ...  Unlike ultrasound (US) examination, the quality of the CT image is not highly operator dependent.  ...  [12] have used the residual convolutional neural network (ResNet) for the classification of focal liver lesions (FLL) such as cysts, focal nodular hyperplasia (FNH), hepatocellular carcinoma (HCC),  ... 
doi:10.38124/ijisrt20jul058 fatcat:ic4ryp6vunfqnb2lfok5syhe44

Deep Fusion Models of Multi-phase CT and Selected Clinical Data for Preoperative Prediction of Early Recurrence in Hepatocellular Carcinoma

Weibin Wang, Qingqing Chen, Yutaro Iwamoto, Panyanat Aonpong, Lanfen Lin, Hongjie Hu, Qiaowei Zhang, Yen-Wei Chen
2020 IEEE Access  
INDEX TERMS Hepatocellular carcinoma, early recurrence, deep learning, multi-phase CT images, clinical data, fusion model.  ...  In this paper, we proposed a deep-learning based prediction model to extract high-level features from the triple-phase CT images and compare its performance with traditional radiomics model and clinical  ...  ACKNOWLEDGMENT (Weibin Wang and Qingqing Chen are co-first authors.)  ... 
doi:10.1109/access.2020.3011145 fatcat:vvw4rpo5ljagze4iyczvth6eda

Nested Dilation Network (NDN) for Multi-Task Medical Image Segmentation

Liansheng Wang, Rongzhen Chen, Shuxin Wanga, Nianyin Zeng, Xiaoyang Huang, Changhua Liu
2019 IEEE Access  
In this paper, we propose a nested dilation network (NDN) which is applied to multiple segmentation tasks even for different modalities, including CT, magnetic resonance imaging (MRI), and endoscopic images  ...  The deep convolutional network has shown excellent performance in medical image analysis.  ...  CT, the segmentation of pancreas in portal venous phase CT and colon polyps in endoscopic images.  ... 
doi:10.1109/access.2019.2908386 fatcat:3x7hbipilzeyfesp7wcly6kj4e

Deep learning workflow in radiology: a primer

Emmanuel Montagnon, Milena Cerny, Alexandre Cadrin-Chênevert, Vincent Hamilton, Thomas Derennes, André Ilinca, Franck Vandenbroucke-Menu, Simon Turcotte, Samuel Kadoury, An Tang
2020 Insights into Imaging  
This article illustrates the workflow for liver lesion detection, segmentation, classification, monitoring, and prediction of tumor recurrence and patient survival.  ...  Interest for deep learning in radiology has increased tremendously in the past decade due to the high achievable performance for various computer vision tasks such as detection, segmentation, classification  ...  For example, in patients with liver metastases, the purpose of lesion classification is to differentiate benign lesions (such as focal liver fat, cysts, and hemangiomas) from malignant lesions (such as  ... 
doi:10.1186/s13244-019-0832-5 pmid:32040647 pmcid:PMC7010882 fatcat:odgm3xc4bbdidlw7qfckuvn3eq

Deep learning in radiology: an overview of the concepts and a survey of the state of the art [article]

Maciej A. Mazurowski, Mateusz Buda, Ashirbani Saha, Mustafa R. Bashir
2018 arXiv   pre-print
We also introduce basic concepts of deep learning including convolutional neural networks. Then, we present a survey of the research in deep learning applied to radiology.  ...  Since the medical field of radiology mostly relies on extracting useful information from images, it is a very natural application area for deep learning, and research in this area has rapidly grown in  ...  Acknowledgments: The authors would like to acknowledge funding from the National Institutes of Biomedical Imaging and Bioengineering grant 5 R01 EB021360.  ... 
arXiv:1802.08717v1 fatcat:7qirj6hb2bdafnplc6au4wysqi

Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI

Maciej A. Mazurowski, Mateusz Buda, Ashirbani Saha, Mustafa R. Bashir
2018 Journal of Magnetic Resonance Imaging  
We also introduce basic concepts of deep learning, including convolutional neural networks. Then, we present a survey of the research in deep learning applied to radiology.  ...  Since the medical field of radiology mainly relies on extracting useful information from images, it is a very natural application area for deep learning, and research in this area has rapidly grown in  ...  Grant Support: The authors would like to acknowledge funding from the National Institutes of Biomedical Imaging and Bioengineering grant 5 R01 EB021360. BIBLIOGRAPHY  ... 
doi:10.1002/jmri.26534 pmid:30575178 pmcid:PMC6483404 fatcat:7jg5sr7z6bbehd6xabsjw6bcde

Deep Neural Architectures for Contrast Enhanced Ultrasound (CEUS) Focal Liver Lesions Automated Diagnosis

Cătălin Daniel Căleanu, Cristina Laura Sîrbu, Georgiana Simion
2021 Sensors  
The aim of the paper is to examine the application of contrast enhanced ultrasound imaging (CEUS) to the problem of automated focal liver lesion (FLL) diagnosis using deep neural networks (DNN).  ...  Our work improves on and broadens previous work in the field in several aspects, e.g., a novel leave-one-patient-out evaluation procedure, which further enabled us to formulate a hard-voting classification  ...  Data Availability Statement: The data presented in this study are available on request from the corresponding author of [33] . The data are not publicly available due to copyright.  ... 
doi:10.3390/s21124126 fatcat:gk4czoj7xfdldlzyiwf27fqvbe

Real-time computer-aided diagnosis of focal pancreatic masses from endoscopic ultrasound imaging based on a hybrid convolutional and long short-term memory neural network model

Anca Loredana Udriștoiu, Irina Mihaela Cazacu, Lucian Gheorghe Gruionu, Gabriel Gruionu, Andreea Valentina Iacob, Daniela Elena Burtea, Bogdan Silviu Ungureanu, Mădălin Ionuț Costache, Alina Constantin, Carmen Florina Popescu, Ștefan Udriștoiu, Adrian Săftoiu (+1 others)
2021 PLoS ONE  
The MLA is based on a deep learning method which combines convolutional (CNN) and long short-term memory (LSTM) neural networks. 2688 images were used for training and 672 images for testing the deep learning  ...  The CNN was developed to identify the discriminative features of images, while a LSTM neural network was used to extract the dependencies between images.  ...  In the current study, we used two deep learning techniques, the Convolution Neural Network (CNN) and Long Short-term Memory (LSTM) models to detect the focal pancreatic masses in four EUS imaging modalities  ... 
doi:10.1371/journal.pone.0251701 pmid:34181680 fatcat:k3nn4dr7wzebxlp45lxgrmgpsu

Recent advancement in cancer detection using machine learning: Systematic survey of decades, comparisons and challenges

Tanzila Saba
2020 Journal of Infection and Public Health  
The aim of the research is to analyze, review, categorize and address the current developments of human body cancer detection using machine learning techniques for breast, brain, lung, liver, skin cancer  ...  Cancer also known as tumor must be quickly and correctly detected in the initial stage to identify what might be beneficial for its cure.  ...  Additionally author is thankful to the anonymous reviewers for their constructive comments and apologize to those researchers whom work is overlooked in this research.  ... 
doi:10.1016/j.jiph.2020.06.033 pmid:32758393 fatcat:sglazth4znh5jjtozguaktruce
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