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PyConvU-Net: a lightweight and multiscale network for biomedical image segmentation

Changyong Li, Yongxian Fan, Xiaodong Cai
2021 BMC Bioinformatics  
Thus, it is significant to develop accurate DL based biomedical image segmentation methods which depend on resources-constraint computing.  ...  Through strictly controlled experiments, PyConvU-Net predictions have a good performance on three biomedical image segmentation tasks with the fewest parameters.  ...  Acknowledgements We thank the referees that reviewed this manuscript for their thoughtful and constructive comments.  ... 
doi:10.1186/s12859-020-03943-2 pmid:33413088 fatcat:ybguxodfurelnoo2hvzo4i47yy

Multi-modality self-attention aware deep network for 3D biomedical segmentation

Xibin Jia, Yunfeng Liu, Zhenghan Yang, Dawei Yang
2020 BMC Medical Informatics and Decision Making  
Firstly, we propose a multi-path encoder and decoder deep network for 3D biomedical segmentation.  ...  Deep learning based on segmentation models have been gradually applied in biomedical images and achieved state-of-the-art performance for 3D biomedical segmentation.  ...  Consent for publication Not applicable.  ... 
doi:10.1186/s12911-020-1109-0 pmid:32646419 fatcat:nz2xx5t7pnfznll3zteug5wkoa

Biomedical Image Segmentation by Retina-like Sequential Attention Mechanism Using Only A Few Training Images [article]

Shohei Hayashi and Bisser Raytchev and Toru Tamaki and Kazufumi Kaneda
2019 arXiv   pre-print
In this paper we propose a novel deep learning-based algorithm for biomedical image segmentation which uses a sequential attention mechanism able to shift the focus of attention across the image in a selective  ...  fully convolutional-based method.  ...  In this paper we concentrate on biomedical image segmentation.  ... 
arXiv:1909.12612v1 fatcat:5uysbiowrnfiza6yqdiqydufei

Improving the performance of convolutional neural network for the segmentation of optic disc in fundus images using attention gates and conditional random fields

Bhargav J Bhatkalkar, Dheeraj R Reddy, Srikanth Prabhu, Sulatha V Bhandary
2020 IEEE Access  
In this paper, we are proposing a novel convolutional neural network architecture for the precise segmentation of the OD in fundus images.  ...  INDEX TERMS Optic disc, attention network, conditional random fields, deep learning, biomedical imaging.  ...  ACKNOWLEDGMENT We thank Kasturba Medical College (KMC), Manipal, India, for providing the fundus image dataset, and TensorFlow Research Cloud (https://www.tensorflow.org/tfrc) for providing the computing  ... 
doi:10.1109/access.2020.2972318 fatcat:b4k4pvupezgp5lw7zp7iwzaqzy

Non-Local Context Encoder: Robust Biomedical Image Segmentation against Adversarial Attacks [article]

Xiang He, Sibei Yang, Guanbin Li?, Haofeng Li, Huiyou Chang, Yizhou Yu
2019 arXiv   pre-print
Recent progress in biomedical image segmentation based on deep convolutional neural networks (CNNs) has drawn much attention.  ...  In addition, NLCE modules can be applied to improve the robustness of other CNN-based biomedical image segmentation methods.  ...  State-of-the-art biomedical image segmentation methods are based on fully convolutional networks (FCN) (Long, Shelhamer, and Darrell 2015) , which is a type of deep convolutional neural networks (CNNs  ... 
arXiv:1904.12181v1 fatcat:3zxbx3zmkffnflboj64obkaknu

Non-Local Context Encoder: Robust Biomedical Image Segmentation against Adversarial Attacks

Xiang He, Sibei Yang, Guanbin Li, Haofeng Li, Huiyou Chang, Yizhou Yu
2019 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
Recent progress in biomedical image segmentation based on deep convolutional neural networks (CNNs) has drawn much attention.  ...  In addition, NLCE modules can be applied to improve the robustness of other CNN-based biomedical image segmentation methods.  ...  State-of-the-art biomedical image segmentation methods are based on fully convolutional networks (FCN) (Long, Shelhamer, and Darrell 2015) , which is a type of deep convolutional neural networks (CNNs  ... 
doi:10.1609/aaai.v33i01.33018417 fatcat:prvvbl542rbwrbkberb5nma3gy

Medical Transformer: Gated Axial-Attention for Medical Image Segmentation [article]

Jeya Maria Jose Valanarasu, Poojan Oza, Ilker Hacihaliloglu, Vishal M. Patel
2021 arXiv   pre-print
Over the past decade, Deep Convolutional Neural Networks have been widely adopted for medical image segmentation and shown to achieve adequate performance.  ...  This motivates us to explore Transformer-based solutions and study the feasibility of using Transformer-based network architectures for medical image segmentation tasks.  ...  Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation.  ... 
arXiv:2102.10662v2 fatcat:3mf6mglv6rdpbm6xf3loco4gz4

Comparison of Deep Learning Algorithms for Semantic Segmentation of Ultrasound Thyroid Nodules

Elmer Jeto Gomes Ataide, Shubham Agrawal, Aishwarya Jauhari, Axel Boese, Alfredol Illanes, Simone Schenke, Michael C. Kreissl, Michael Friebe
2021 Current Directions in Biomedical Engineering  
in terms of thyroid nodule segmentation in US images.  ...  This work presents a comparison between four state of the art (SOTA) Deep Learning segmentation algorithms (UNet, SUMNet, ResUNet and Attention UNet).  ...  (DCNN)[5], fully convolutional neural networks (FCN)[6], and its variants[7].  ... 
doi:10.1515/cdbme-2021-2224 fatcat:hi7n5tzhizampfvnyzziryk2i4

Gated Dense Convolutional Neural Networks for Unbalanced Representations in STEM Tomography

Arda Genc, Libor Kovarik, Hamish L. Fraser
2022 Microscopy and Microanalysis  
Brox, U-Net: Convolutional Networks for Biomedical Image Segmentation, In International Conference on Medical Image Computing and Computer-Assisted Intervention, 9351 (2015) 234-241. [2] A.  ...  for Pt nanoparticle segmentation with attention units.  ... 
doi:10.1017/s1431927622011667 fatcat:63dzafghqja5ditkj7pbfqqks4

MDA-Net: Multi-Dimensional Attention-Based Neural Network for 3D Image Segmentation [article]

Rutu Gandhi, Yi Hong
2021 arXiv   pre-print
To address this challenge, we propose a multi-dimensional attention network (MDA-Net) to efficiently integrate slice-wise, spatial, and channel-wise attention into a U-Net based network, which results  ...  Segmenting an entire 3D image often has high computational complexity and requires large memory consumption; by contrast, performing volumetric segmentation in a slice-by-slice manner is efficient but  ...  Acknowledgements No funding was received for conducting this study. The authors have no relevant financial or non-financial interests to disclose.  ... 
arXiv:2105.04508v1 fatcat:6gf4hb2u7vfhtbyf7qx7mvuzmu

Designing a High-Performance Deep Learning Theoretical Model for Biomedical Image Segmentation by Using Key Elements of the Latest U-Net-Based Architectures

Andreea Roxana Luca, Tudor Florin Ursuleanu, Liliana Gheorghe, Roxana Grigorovici, Stefan Iancu, Maria Hlusneac, Cristina Preda, Alexandru Grigorovici
2021 Journal of Computer and Communications  
an improvement added on convolutional network architecture for fast and precise segmentation of images (Attention U-Net), Harmony Densely Connected Network-Medical image Segmentation (HarDNet-MSEG).  ...  We aim to design a high-performance deep learning model, combined from convolutional neural network (U-Net)-based architectures, for segmentation of the medical image that is independent of the type of  ...  DL architectures designed for diagnosis-segmentation of medical images, three categories can be exemplified: • FCN-based models (fully convolutional network) [1] [2] ; • Convolutional Neural Network  ... 
doi:10.4236/jcc.2021.97002 fatcat:zhemcnvvt5egxl6u3liltjfsim

Recent Advances in Biomedical Image Segmentation Using Neural Networks

Cecilia Irene Loeza Mejía, Balzhoyt Roldán Ortega, Rajesh Roshan Biswal, Gregorio Fernández Lambert, D. Reyes González
2020 Research in Computing Science  
This work presents a comparison of different methods including deep learning for segmentation of multimodal biomedical images.  ...  However, there is not a single method or solution because of the variation in the property of images, medical imaging techniques and modalities, variability and noise for each object of interest.  ...  U-Net Convolutional Networks for Biomedical Image Segmentation It is a novel framework of CNN designed for precise and fast segmentation of images [13] . 3D-SkipDenseSeg 3D Fully Convolutional, Skip-Connected  ... 
dblp:journals/rcs/MejiaOBLG20 fatcat:bun3iweysvgbtlocyxgbrzyjcy

Modality specific U-Net variants for biomedical image segmentation: A survey [article]

Narinder Singh Punn, Sonali Agarwal
2022 arXiv   pre-print
With the advent of advancements in deep learning approaches, such as deep convolution neural network, residual neural network, adversarial network; U-Net architectures are most widely utilized in biomedical  ...  Finally, the strengths and similarities of these U-Net variants are analysed along with the challenges involved in biomedical image segmentation to uncover promising future research directions in this  ...  Acknowledgment We thank our institute, Indian Institute of Information Technology Allahabad (IIITA), India and Big Data Analytics (BDA) lab for allocating the necessary  ... 
arXiv:2107.04537v4 fatcat:m5oqea5q6vhbhkerjmejder3hu

Upgraded W-Net with Attention Gates and its Application in Unsupervised 3D Liver Segmentation [article]

Dhanunjaya Mitta, Soumick Chatterjee, Oliver Speck, Andreas Nürnberger
2020 arXiv   pre-print
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.  ...  In addition, to suppress noise in the segmentation we added attention gates to the skip connections.  ...  FUTURE WORK This paper stands as a proof of concept for unsupervised biomedical image segmentation using the proposed 3D Attention W-Net.  ... 
arXiv:2011.10654v1 fatcat:pb65aaaq3vagfn2vlwmz3unmuu

MDU-Net: Multi-scale Densely Connected U-Net for biomedical image segmentation [article]

Jiawei Zhang, Yuzhen Jin, Jilan Xu, Xiaowei Xu, Yanchun Zhang
2018 arXiv   pre-print
In the light of the fully convolutional networks (FCN) and U-Net, deep convolutional networks (DNNs) have made significant contributions in biomedical image segmentation applications.  ...  In this paper, based on U-Net, we propose MDUnet, a multi-scale densely connected U-net for biomedical image segmentation. we propose three different multi-scale dense connections for U shaped architectures  ...  Based on fully convolu- tional networks (FCN) and U-Net [31, 26] , deep convolutional networks (DNNs) have made significant improvemnents in biomedical image segmentation.  ... 
arXiv:1812.00352v2 fatcat:vqqjezegwjfjdfjtxwma2olg2i
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