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Deeply Self-Supervised Contour Embedded Neural Network Applied to Liver Segmentation
[article]
2019
arXiv
pre-print
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To guide a neural network to accurately delineate a target liver object, the network was deeply supervised by applying the adaptive self-supervision scheme to derive the essential contour, which acted ...
Significance: In this study, a new framework was introduced to guide a neural network and learn complementary contour features. ...
Deeply supervised networks [36] have been developed to hierarchically supervise multiple layers and segment medical images [31] . ...
arXiv:1808.00739v5
fatcat:24r7mzcbsfdz7njb2jttsgkewm
Automatic segmentation of kidney and liver tumors in CT images
[article]
2019
arXiv
pre-print
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To address the problem more and more researchers rely on assistance of deep convolutional neural networks (CNN) with 2D or 3D type architecture that have proven to be effective in a wide range of computer ...
The described method was then applied to the 2019 Kidney Tumor Segmentation (KiTS-2019) challenge, where our single submission achieved 96.38% for kidney and 67.38% for tumor Dice scores. ...
[12] developed a 3D version of the VGG-FCN [6] architecture with deep supervision to hidden layers, so-called 3D deeply supervised network (3D DSN), which could accelerate the optimization convergence ...
arXiv:1908.01279v2
fatcat:i5huxcjiajepvjzepm257ozm6y
Deep Learning in Multi-organ Segmentation
[article]
2020
arXiv
pre-print
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These methods were classified into six categories according to their network design. ...
This paper presents a review of deep learning (DL) in multi-organ segmentation. We summarized the latest DL-based methods for medical image segmentation and applications. ...
INTRODUCTION
Post-processing Post-processing is applied to refine the segmented contours to be more smooth, continuous and realistic. ...
arXiv:2001.10619v1
fatcat:6uwqwnzydzccblh5cajhsgdpea
Progress of Machine Vision in the Detection of Cancer Cells in Histopathology
2022
IEEE Access
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disadvantages of existing methods in image preprocessing, segmentation, feature extraction and recognition. ...
Finally, research on the detection methods of histopathological cancer cells is reviewed and prospected, and future development trends are predicted to provide guidance for follow-up research. ...
NEURAL NETWORK With the development of deep learning technology, neural network methods have been applied to pathological image segmentation. Wang et al. ...
doi:10.1109/access.2022.3161575
fatcat:uzj3rxfpqjg5xpy2sjdjjk2j5i
A Progressively-trained Scale-invariant and Boundary-aware Deep Neural Network for the Automatic 3D Segmentation of Lung Lesions
[article]
2018
arXiv
pre-print
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In summary, by leveraging the limited 2D delineations on the RECIST-slices, P-SiBA is an effective semi-supervised approach to produce accurate lesion segmentations in 3D. ...
To extend the 2D segmentations to 3D, we propose a volumetric progressive lesion segmentation (PLS) algorithm to automatically segment the 3D lesion volume from 2D delineations using a scale-invariant ...
Visualization of lesion segmentations obtained using P-SiBA (green contours) compared to the ground-truth segmentations (red contours). 3D CT volumes with segmentation contours are displayed in 2D axial ...
arXiv:1811.04437v1
fatcat:br7ohmtcn5gqvlvreovxmqzphm
Bata-Unet: Deep Learning Model for Liver Segmentation
2020
Signal & Image Processing An International Journal
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In this paper we aim to enhance our previous work which we were proposed a Batch Normalization After All - Convolutional Neural Network (BATA-Convnet) model to segment the liver, where the Dice is equal ...
There are many semiautomatic and fully automatic approaches have been proposed to improve the liver segmentation procedure some of them use deep learning techniques for Segmentation and other one use a ...
They proposed an auto-context neural network; it achieved an effective estimation to obtain the shape prior. They use a self-supervised contour scheme to extend their network. ...
doi:10.5121/sipij.2020.11505
fatcat:jrr2hzn47bbq7hksnmq3jhk47y
Exploiting full Resolution Feature Context for Liver Tumor and Vessel Segmentation via Integrate Framework: Application to Liver Tumor and Vessel 3D Reconstruction under embedded microprocessor
[article]
2022
arXiv
pre-print
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2022 pre-print arXiv:2111.13299v3 fatcat:bmu4te75orbhvmhehj2vf3npwiOther Versions
This network achieved very competitive performance for liver vessel and liver tumor segmentation tasks, meanwhile it can improve the recognition of morphologic margins of liver tumors by exploiting the ...
Segmentation and labeling of liver tumors and blood vessels in CT images can provide convenience for doctors in liver tumor diagnosis and surgical intervention. ...
[24] proposed UNet++, a model that combines a deeply supervised encoder and decoder and links the sub-networks of both through a series of hops as a way to reduce the semantic gap between the encoder ...
arXiv:2111.13299v4
fatcat:v5wymgybkfccrltkjfweezegva
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 ...
The study highlights how cancer diagnosis, cure process is assisted using machine learning with supervised, unsupervised and deep learning techniques. ...
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
3D IFPN: Improved Feature Pyramid Network for Automatic Segmentation of Gastric Tumor
2021
Frontiers in Oncology
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Moreover, to explore the generalization for other segmentation tasks, we also extend the proposed network to liver tumor segmentation in CT images of the MICCAI 2017 Liver Tumor Segmentation Challenge. ...
Furthermore, a stage-wise deep supervision (SDS) mechanism and a hybrid loss function are also embedded to enhance the feature learning ability of the network. ...
A stage-wise deep supervision (SDS) mechanism is introduced to improve the traditional deeply supervised nets (DSN) (19) by reducing the weight number of the final prediction. ...
doi:10.3389/fonc.2021.618496
pmid:34094903
pmcid:PMC8173118
fatcat:63zl5gbkrrej5lubx7z2syqznm
CE-Net: Context Encoder Network for 2D Medical Image Segmentation
2019
IEEE Transactions on Medical Imaging
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2019 pre-print arXiv:1903.02740v1 fatcat:25hv2mgydfcwrkvi7btjdv7jte
We applied the proposed CE-Net to different 2D medical image segmentation tasks. ...
With the rapid development of a convolutional neural network in image processing, deep learning has been used for medical image segmentation, such as optic disc segmentation, blood vessel detection, lung ...
[46] proposed a Voxresnet to segment volumetric brain, and Dou et al. [47] proposed 3D deeply supervised network (3D DSN) to automatically segment lung in CT volumes. ...
doi:10.1109/tmi.2019.2903562
pmid:30843824
fatcat:b7p7plxshfhfrk76z76v6pvvyu
A Review on Deep Learning in Minimally Invasive Surgery
2021
IEEE Access
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They achieved better results compared to non-pretrained networks. Their work is available at GitLab 18 . A self-supervised method is also proposed by Chittajallu et al. ...
To consider the temporal dependencies in the input data, we use Recurrent Neural Networks (RNNs). Unlike feedforward neural networks, the processing units in an RNN form a cycle. ...
She is currently an Associate Professor and is responsible for a variety of subjects related to robotics. ...
doi:10.1109/access.2021.3068852
fatcat:gfpghqfptzdktlody5z263cdju
Learning Neural Textual Representations for Citation Recommendation
2021
2020 25th International Conference on Pattern Recognition (ICPR)
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to Liver
Imaging
DAY 3 -Jan 14, 2021
Kelm, André Peter; Zölzer, Udo
1125
Walk the Lines: Object Contour Tracing CNN for Contour
Completion of Ships
DAY 3 -Jan 14, 2021
Xin, Ning; Xu, Shaohui ...
the Meta-Learning Idea Able to Improve the Generalization of
Deep Neural Networks on the Standard Supervised Learning? ...
doi:10.1109/icpr48806.2021.9412725
fatcat:3vge2tpd2zf7jcv5btcixnaikm
Deep Learning in Medical Image Registration: A Review
[article]
2019
arXiv
pre-print
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These methods were classified into seven categories according to their methods, functions and popularity. ...
A short assessment was presented following the detailed review of each category to summarize its achievements and future potentials. ...
Supervision methods As neural network develops, many new supervision terms such as 'supervised', 'unsupervised', 'deeply supervised', 'weakly supervised', 'dual supervised', 'self-supervised' have emerged ...
arXiv:1912.12318v1
fatcat:kuvckosqd5hp7asg6dofhuiis4
Deep Learning in Cardiology
2019
IEEE Reviews in Biomedical Engineering
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2019 pre-print arXiv:1902.11122v1 fatcat:xeollgq57fb3zkwmxnm5uwo7kmOther Versions
The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. ...
We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use. ...
3D FCN with dilations (HVS16)
69.5%
Yu 2017 [151]
CNN
deeply supervised 3D FCN constructed in a self-similar fractal scheme (HVS16)
multiple
Payer [152]
CNN
FCN for localization and another FCN ...
doi:10.1109/rbme.2018.2885714
fatcat:pa47trmskvflvig5cotth265q4
State-of-the-Art Challenges and Perspectives in Multi-Organ Cancer Diagnosis via Deep Learning-Based Methods
2021
Cancers
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In this survey, we analyze the state-of-the-art approaches for multi-organ cancer detection, segmentation, and classification. ...
It consists of abnormally expanding areas that are threatening to human survival. Hence, the timely detection of cancer is important to expanding the survival rate of patients. ...
Acknowledgments: Authors would like to thank National Key R&D Program of China for providing experimental facilities to conduct this study. ...
doi:10.3390/cancers13215546
pmid:34771708
pmcid:PMC8583666
fatcat:3xavsdok7zdp5oa2ix2gtkolbq
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