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A Tetrahedron-Based Heat Flux Signature for Cortical Thickness Morphometry Analysis
[chapter]
2018
Lecture Notes in Computer Science
Preserved Fulltext
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]
2021
arXiv
pre-print
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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
2020
Korean Journal of Radiology
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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
2021
Diagnostics
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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
2021
Translational Gastroenterology and Hepatology
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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]
2018
Lecture Notes in Computer Science
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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
2020
International Journal of Innovative Science and Research Technology
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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
2020
IEEE Access
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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
2019
IEEE Access
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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
2020
Insights into Imaging
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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]
2018
arXiv
pre-print
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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
2018
Journal of Magnetic Resonance Imaging
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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
2021
Sensors
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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
2021
PLoS ONE
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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
2020
Journal of Infection and Public Health
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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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