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Deeply Self-Supervised Contour Embedded Neural Network Applied to Liver Segmentation [article]

Minyoung Chung, Jingyu Lee, Minkyung Lee, Jeongjin Lee, Yeong-Gil Shin
2019 arXiv   pre-print
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]

Dina B. Efremova, Dmitry A. Konovalov, Thanongchai Siriapisith, Worapan Kusakunniran, Peter Haddawy
2019 arXiv   pre-print
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]

Yang Lei, Yabo Fu, Tonghe Wang, Richard L.J. Qiu, Walter J. Curran, Tian Liu, Xiaofeng Yang
2020 arXiv   pre-print
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

Wenbin He, Yongjie Han, Wuyi Ming, Jinguang Du, Yinxia Liu, Yuan Yang, Leijie Wang, Yongqiang Wang, Zhiwen Jiang, Chen Cao, Jie Yuan
2022 IEEE Access  
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]

Bo Zhou, Randolph Crawford, Belma Dogdas, Gregory Goldmacher, Antong Chen
2018 arXiv   pre-print
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

Fatima Abdalbagi, Serestina Viriri, Mohammed Tajalsir Mohammed
2020 Signal & Image Processing An International Journal  
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]

Xiangyu Meng, Xudong Zhang, Gan Wang, Ying Zhang, Xin Shi, Huanhuan Dai, Zixuan Wang, Xun Wang
2022 arXiv   pre-print
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

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  ...  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

Haimei Li, Bing Liu, Yongtao Zhang, Chao Fu, Xiaowei Han, Lei Du, Wenwen Gao, Yue Chen, Xiuxiu Liu, Yige Wang, Tianfu Wang, Guolin Ma (+1 others)
2021 Frontiers in Oncology  
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

Zaiwang Gu, Jun Cheng, Huazhu Fu, Kang Zhou, Huaying Hao, Yitian Zhao, Tianyang Zhang, Shenghua Gao, Jiang Liu
2019 IEEE Transactions on Medical Imaging  
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

Irene Rivas-Blanco, Carlos J. Perez-Del-Pulgar, Isabel Garcia-Morales, Victor F. Munoz
2021 IEEE Access  
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

Binh Thanh Kieu, Inigo Jauregi Unanue, Son Bao Pham, Hieu Xuan Phan, Massimo Piccardi
2021 2020 25th International Conference on Pattern Recognition (ICPR)  
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]

Yabo Fu, Yang Lei, Tonghe Wang, Walter J. Curran, Tian Liu, Xiaofeng Yang
2019 arXiv   pre-print
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

Paschalis Bizopoulos, Dimitrios Koutsouris
2019 IEEE Reviews in Biomedical Engineering  
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

Saqib Ali, Jianqiang Li, Yan Pei, Rooha Khurram, Khalil ur Rehman, Abdul Basit Rasool
2021 Cancers  
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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