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MC-UNet Multi-module Concatenation based on U-shape Network for Retinal Blood Vessels Segmentation [article]

Ting Zhang, Jun Li, Yi Zhao, Nan Chen, Han Zhou, Hongtao Xu, Zihao Guan, Changcai Yang, Lanyan Xue, Riqing Chen, Lifang Wei
2022 arXiv   pre-print
A novel U-shaped network named Multi-module Concatenation which is based on Atrous convolution and multi-kernel pooling is put forward to retinal vessels segmentation in this paper.  ...  Many deep learning frameworks have come up for retinal blood vessels segmentation tasks.  ...  a novel U-shape network is proposed named Multi-module Concatenation U-Net (MC-UNet) based on atrous convolution and multi-kernel pooling for retinal vessels segmentation.  ... 
arXiv:2204.03213v1 fatcat:dw6lrbx2qrfcdlp7rae6cht5lu

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

David Ahmedt-Aristizabal, Mohammad Ali Armin, Simon Denman, Clinton Fookes, Lars Petersson
2021 Sensors  
We also outline the limitations of existing techniques and discuss potential directions for future research.  ...  We provide an overview of these methods in a systematic manner, organized by their domain of application including functional connectivity, anatomical structure, and electrical-based analysis.  ...  [141] also incorporated a GCN into a unified CNN architecture for 2D vessel segmentation on retinal image datasets.  ... 
doi:10.3390/s21144758 fatcat:jytyt4u2pjgvhnhcto3vcvd3a4

Vision Transformers in Medical Computer Vision – A Contemplative Retrospection [article]

Arshi Parvaiz, Muhammad Anwaar Khalid, Rukhsana Zafar, Huma Ameer, Muhammad Ali, Muhammad Moazam Fraz
2022 arXiv   pre-print
We surveyed the application of Vision transformers in different areas of medical computer vision such as image-based disease classification, anatomical structure segmentation, registration, region-based  ...  We hope that this review article will open future directions for researchers in medical computer vision.  ...  [202] established a module MSAM, Multi model Spatial Attention Module, a deep learning based framework for lung tumor segmentation in PET-CT.  ... 
arXiv:2203.15269v1 fatcat:wecjpoikbvfz5cygytqpktoxdq

Myocardial Segmentation of Cardiac MRI Sequences With Temporal Consistency for Coronary Artery Disease Diagnosis

Yutian Chen, Wen Xie, Jiawei Zhang, Hailong Qiu, Dewen Zeng, Yiyu Shi, Haiyun Yuan, Jian Zhuang, Qianjun Jia, Yanchun Zhang, Yuhao Dong, Meiping Huang (+1 others)
2022 Frontiers in Cardiovascular Medicine  
Coronary artery disease (CAD) is the most common cause of death globally, and its diagnosis is usually based on manual myocardial (MYO) segmentation of MRI sequences.  ...  In this article, we propose a MYO segmentation framework for sequence of cardiac MRI (CMR) scanning images of the left ventricular (LV) cavity, right ventricular (RV) cavity, and myocardium.  ...  Pyramid U-Net for retinal vessel segmentation. In: ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Toronto, ON: IEEE (2021). p. 1125–9. 27.  ... 
doi:10.3389/fcvm.2022.804442 pmid:35282363 pmcid:PMC8914019 fatcat:2w4jateounhydhs4kspxfozefa

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future [article]

David Ahmedt-Aristizabal, Mohammad Ali Armin, Simon Denman, Clinton Fookes, Lars Petersson
2021 arXiv   pre-print
We also outline the limitations of existing techniques and discuss potential directions for future research.  ...  We provide an overview of these methods in a systematic manner, organized by their domain of application including functional connectivity, anatomical structure and electrical-based analysis.  ...  [187] also incorporated a GCN into a unified CNN architecture for 2D vessel segmentation on retinal image datasets.  ... 
arXiv:2105.13137v1 fatcat:gm7d2ziagba7bj3g34u4t3k43y

Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19

Hanan Farhat, George E. Sakr, Rima Kilany
2020 Machine Vision and Applications  
Yet, coronavirus can be the real trigger to open the route for fast integration of DL in hospitals and medical centers.  ...  Likewise, deep learning applications (DL) on pulmonary medical images emerged to achieve remarkable advances leading to promising clinical trials.  ...  It proposed a model based on multi-stream multi-scale convolutional networks that aim to classify all nodule types without the need for any information.  ... 
doi:10.1007/s00138-020-01101-5 pmid:32834523 pmcid:PMC7386599 fatcat:tkkylrptc5hkpoj52hjs3kuttu

Advancing efficiency and robustness of neural networks for imaging

Konstantinos Kamnitsas, Benjamin Glocker, Daniel Rueckert
2020
It investigates domain adaptation and introduces an architecture for adversarial networks tailored for adaptation of segmentation networks.  ...  Moreover, it explores strategies for achieving robust performance on unseen data.  ...  Contributions We present a fully automatic approach for lesion segmentation in multi-modal brain MRI based on an 11-layers deep, multi-scale, 3D CNN with the following main contributions: 1.  ... 
doi:10.25560/80157 fatcat:mv3q2zargfamrifgqwfycd53mq