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Efficient Folded Attention for Medical Image Reconstruction and Segmentation

Hang Zhang, Jinwei Zhang, Rongguang Wang, Qihao Zhang, Pascal Spincemaille, Thanh D. Nguyen, Yi Wang
2021 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
Recently, 3D medical image reconstruction (MIR) and segmentation (MIS) based on deep neural networks have been developed with promising results, and attention mechanism has been further designed for performance  ...  In this paper, we propose a folded attention (FA) approach to improve the computational efficiency of traditional attention methods on 3D medical images.  ...  We argue that a unified attention approach that considers both the spatial-channel dependency and the efficiency of computation is of great practical value for modern 3D Medical image tasks.  ... 
doi:10.1609/aaai.v35i12.17298 fatcat:h6l5tgaxvbbtbbsfoseh3wkkfm

Efficient Folded Attention for 3D Medical Image Reconstruction and Segmentation [article]

Hang Zhang, Jinwei Zhang, Rongguang Wang, Qihao Zhang, Pascal Spincemaille, Thanh D. Nguyen, Yi Wang
2020 arXiv   pre-print
Recently, 3D medical image reconstruction (MIR) and segmentation (MIS) based on deep neural networks have been developed with promising results, and attention mechanism has been further designed to capture  ...  In this paper, we propose a folded attention (FA) approach to improve the computational efficiency of traditional attention methods on 3D medical images.  ...  We argue that a unified attention approach that considers both the spatial-channel dependency and the efficiency of computation is of great practical value for modern 3D Medical image tasks.  ... 
arXiv:2009.05576v1 fatcat:x2yictbsobgy5gd67nqlqioqde

Multi-scale self-guided attention for medical image segmentation [article]

Ashish Sinha, Jose Dolz
2020 arXiv   pre-print
This demonstrates the efficiency of our approach to generate precise and reliable automatic segmentations of medical images.  ...  Even though convolutional neural networks (CNNs) are driving progress in medical image segmentation, standard models still have some drawbacks.  ...  This demonstrates the efficiency of our approach to provide precise and reliable automatic segmentations of medical images.  ... 
arXiv:1906.02849v3 fatcat:vga5xpkgivhsxaymk74owyhk7a

SAU-Net: Efficient 3D Spine MRI Segmentation Using Inter-Slice Attention

Yichi Zhang, Lin Yuan, Yujia Wang, Jicong Zhang
2020 International Conference on Medical Imaging with Deep Learning  
However, 3D CNN suffers from higher computational cost, memory cost and risk of over-fitting, especially for medical images where the number of labeled data is limited.  ...  To address these problems, we apply the attention mechanism for the utilization of inter-slice information in 3D segmentation tasks based on 2D convolutional networks and propose a spatial attention-based  ...  For medical image segmentation, (Ashish and Dolz, 2019) proposed a multi-level attention based architecture for abdominal organ segmentation from MR images.  ... 
dblp:conf/midl/ZhangYWZ20 fatcat:fqyxg7ttgfhorlbrzizk4dtifm

Optimal MRI Undersampling Patterns for Ultimate Benefit of Medical Vision Tasks [article]

Artem Razumov, Oleg Y. Rogov, Dmitry V. Dylov
2021 arXiv   pre-print
We find the optimal undersampling patterns in k-space that maximize target value functions of interest in commonplace medical vision problems (reconstruction, segmentation, and classification) and propose  ...  We validate the proposed MRI acceleration paradigm on three classical medical datasets, demonstrating a noticeable improvement of the target metrics at the high acceleration factors (for the segmentation  ...  The common medical image analysis problems considered herein are segmentation, classification, and image reconstruction from the raw undersampled k-space data.  ... 
arXiv:2108.04914v1 fatcat:muqf3kexzjetbo7og6yminj3ei

Technical note: Accelerated nonrigid motion-compensated isotropic 3D coronary MR angiography

Teresa Correia, Gastão Cruz, Torben Schneider, René M. Botnar, Claudia Prieto
2017 Medical Physics (Lancaster)  
However, a slight reduced visibility of the distal segment of the RCA can be observed for subjects 6 and 7 in the ACOMoCo reconstructions.  ...  Images were reconstructed using NMC and the proposed ACOMoCo method from three-fold undersampled datasets, acquired using a VD-CAPR trajectory with isotropic resolution.  ... 
doi:10.1002/mp.12663 pmid:29131353 pmcid:PMC5814733 fatcat:7zysugafizf2ne7dfuvwt7defu

Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images [article]

Ali Hatamizadeh, Vishwesh Nath, Yucheng Tang, Dong Yang, Holger Roth, Daguang Xu
2022 arXiv   pre-print
In recent years, Fully Convolutional Neural Networks (FCNNs) approaches have become the de facto standard for 3D medical image segmentation.  ...  Semantic segmentation of brain tumors is a fundamental medical image analysis task involving multiple MRI imaging modalities that can assist clinicians in diagnosing the patient and successively studying  ...  However, UNETR has shown better performance in terms of both accuracy and efficiency in different medical image segmentation tasks [16] .  ... 
arXiv:2201.01266v1 fatcat:kst2ddakorayfl4zfpimuacspy

A Deep Attentive Convolutional Neural Network for Automatic Cortical Plate Segmentation in Fetal MRI [article]

Haoran Dou, Davood Karimi, Caitlin K. Rollins, Cynthia M. Ortinau, Lana Vasung, Clemente Velasco-Annis, Abdelhakim Ouaalam, Xin Yang, Dong Ni, Ali Gholipour
2021 arXiv   pre-print
Fetal cortical plate segmentation is essential in quantitative analysis of fetal brain maturation and cortical folding.  ...  Results show that our method outperforms several state-of-the-art deep models for segmentation, as well as a state-of-the-art multi-atlas segmentation technique.  ...  Our comparisons also showed that our proposed model was superior to prior attentive models for medical image segmentation [39] , [40] .  ... 
arXiv:2004.12847v3 fatcat:r26i6yn4tnhhrn66ld2kfnrp6m

A Data-Efficient Deep Learning Framework for Segmentation and Classification of Histopathology Images [article]

Pranav Singh, Jacopo Cirrone
2022 arXiv   pre-print
While these questions can be partially answered using traditional methods, artificial intelligence approaches for segmentation and classification provide a much more efficient method to understand the  ...  The current study of cell architecture of inflammation in histopathology images commonly performed for diagnosis and research purposes excludes a lot of information available on the biopsy slide.  ...  Elena Sizikova (Moore Sloan Faculty Fellow, Center for Data Science (CDS), New York University (NYU)) for her valuable feedback.  ... 
arXiv:2207.06489v3 fatcat:4op2dzfvfnftljcqsixrzn3mdi

Weaving Attention U-net: A Novel Hybrid CNN and Attention-based Method for Organs-at-risk Segmentation in Head and Neck CT Images [article]

Zhuangzhuang Zhang, Tianyu Zhao, Hiram Gay, Weixiong Zhang, Baozhou Sun
2021 arXiv   pre-print
Our results of the new Weaving Attention U-net demonstrate superior or similar performance on the segmentation of head and neck CT images.  ...  We aim to develop a novel hybrid deep learning approach, combining convolutional neural networks (CNNs) and the self-attention mechanism, for rapid and accurate multi-organ segmentation on head and neck  ...  ACKNOWLEDGEMENTS We thank Varian Medical System for their financial support through a research grant. REFERENCE  ... 
arXiv:2107.04847v2 fatcat:4air6q4bzrfllohhyssthouhea

Neural Architecture Search for Compressed Sensing Magnetic Resonance Image Reconstruction [article]

Jiangpeng Yan, Shuo Chen, Yongbing Zhang, Xiu Li
2020 arXiv   pre-print
for other medical image applications.  ...  Inspired by this, here we proposed a novel and efficient network for the MR image reconstruction problem via NAS instead of manual attempts.  ...  Rui Li, Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University for their valuable discussions that significantly improved the quality of the  ... 
arXiv:2002.09625v5 fatcat:wtlefgdobjavfkkciqnuvmexje

Application-Driven MRI: Joint Reconstruction and Segmentation from Undersampled MRI Data [chapter]

Jose Caballero, Wenjia Bai, Anthony N. Price, Daniel Rueckert, Joseph V. Hajnal
2014 Lecture Notes in Computer Science  
Medical image segmentation has traditionally been regarded as a separate process from image acquisition and reconstruction, even though its performance directly depends on the quality and characteristics  ...  In this paper we propose a joint segmentation and reconstruction algorithm for undersampled magnetic resonance data.  ...  We perform CS reconstruction followed by GMM segmentation on 10-fold accelerated scans and compare them with SegMRI segmentation.  ... 
doi:10.1007/978-3-319-10404-1_14 pmid:25333107 fatcat:l3zmukflzvhwfnzhapc4rws7ci

Design a model of Image Restoration using AI in Digital Image Processing

Boosi Shyamala, Dr. Chetana Tukkoji, Archana S Nadhan, Dioline Sara
2021 Turkish Journal of Computer and Mathematics Education  
Image restoration is the process of obtaining a distorted/noise image and giving an approximate clear image of the original image. False focus, motion blur and noise are forms of distortion.  ...  Modern artificial intelligence (AI) applied to image processing includes facial recognition, object recognition and detection, video, image action, and visual search.  ...  For multimode medical image segmentation, the fusion strategy plays an important role in achieving accurate segmentation results.  ... 
doi:10.17762/turcomat.v12i5.1497 fatcat:5uze4pz3ybblfagx2nxvvjcxfq

IntrA: 3D Intracranial Aneurysm Dataset for Deep Learning [article]

Xi Yang, Ding Xia, Taichi Kin, Takeo Igarashi
2020 arXiv   pre-print
In this paper, instead of 2D medical images, we introduce an open-access 3D intracranial aneurysm dataset, IntrA, that makes the application of points-based and mesh-based classification and segmentation  ...  Our dataset can be used to diagnose intracranial aneurysms and to extract the neck for a clipping operation in medicine and other areas of deep learning, such as normal estimation and surface reconstruction  ...  To date, almost all of them are 2D medical images. Non-medical 3D dataset.  ... 
arXiv:2003.02920v2 fatcat:ecsico6livgwpbmlqgjyuijvwe

ASC-Net: Unsupervised Medical Anomaly Segmentation Using an Adversarial-based Selective Cutting Network [article]

Raunak Dey, Wenbo Sun, Haibo Xu, Yi Hong
2021 arXiv   pre-print
Our ASC-Net learns from normal and abnormal medical scans to segment anomalies in medical scans without any masks for supervision.  ...  In this paper we consider the problem of unsupervised anomaly segmentation in medical images, which has attracted increasing attention in recent years due to the expensive pixel-level annotations from  ...  Acknowledgements This work was supported by Shanghai Municipal Science and Technology Major Project 2021SHZDZX0102 and NSF 1755970.  ... 
arXiv:2112.09135v1 fatcat:gqnklo7kojbrlbz5navgn7slgq
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