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An Annotation Sparsification Strategy for 3D Medical Image Segmentation via Representative Selection and Self-Training

Hao Zheng, Yizhe Zhang, Lin Yang, Chaoli Wang, Danny Z. Chen
2020 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
We achieve this by (i) selecting representative slices in 3D images that minimize data redundancy and save annotation effort, and (ii) self-training with pseudo-labels automatically generated from the  ...  In this paper, we propose a new DL framework for reducing annotation effort and bridging the gap between full annotation and sparse annotation in 3D medical image segmentation.  ...  Acknowledgments This research was supported in part by NSF grants CCF-1617735, CNS-1629914 and IIS-1455886, and NIH grant R01 DE027677-01.  ... 
doi:10.1609/aaai.v34i04.6175 pmid:33274122 pmcid:PMC7710151 fatcat:7enptojitrcspn5p4lc4tyxiz4

A Collaborative Computer Aided Diagnosis (C-CAD) System with Eye-Tracking, Sparse Attentional Model, and Deep Learning [article]

Naji Khosravan, Haydar Celik, Baris Turkbey, Elizabeth Jones, Bradford Wood, Ulas Bagci
2018 arXiv   pre-print
tumors, how they interact with the information in an image, and how they search for unknown pathology in the images.  ...  In parallel with efforts in computerized analysis of radiology scans, several researchers have examined behaviors of radiologists while screening medical images to better understand how and why they miss  ...  Acknowledgement We would like to thank Nancy Terry, NIH Library Editing Service, for reviewing the manuscript.  ... 
arXiv:1802.06260v2 fatcat:3ufrgodf3va7dcsmii63mzunpe

Uncertainty-Aware Body Composition Analysis with Deep Regression Ensembles on UK Biobank MRI [article]

Taro Langner, Fredrik K. Gustafsson, Benny Avelin, Robin Strand, Håkan Ahlström, Joel Kullberg
2021 arXiv   pre-print
Along with rich health-related metadata, medical images have been acquired for over 40,000 male and female UK Biobank participants, aged 44-82, since 2014.  ...  Despite the potential for high speed and accuracy, these networks produce no output segmentations that could indicate the reliability of individual measurements.  ...  Acknowledgment This work was supported by a research grant from the Swedish Heart-Lung Foundation and the Swedish Research Council (2016-01040, 2019-04756, 2020-0500, 2021-70492) and used the UK Biobank  ... 
arXiv:2101.06963v3 fatcat:m5ms7watejaz5dvlhlqpxgi5qy

PDC-Net+: Enhanced Probabilistic Dense Correspondence Network [article]

Prune Truong and Martin Danelljan and Radu Timofte and Luc Van Gool
2021 arXiv   pre-print
Moreover, we develop an architecture and an enhanced training strategy tailored for robust and generalizable uncertainty prediction in the context of self-supervised training.  ...  We further validate the usefulness of our probabilistic confidence estimation for the tasks of pose estimation, 3D reconstruction, image-based localization, and image retrieval.  ...  ACKNOWLEDGMENTS This work was supported by the ETH Zürich Fund (OK), a Huawei Gift, Huawei Technologies Oy (Finland), Amazon AWS, and an Nvidia GPU grant.  ... 
arXiv:2109.13912v2 fatcat:loqjlmmnbjfkxmyvfhqmy7ittq

A Survey of Visual Transformers [article]

Yang Liu, Yao Zhang, Yixin Wang, Feng Hou, Jin Yuan, Jiang Tian, Yang Zhang, Zhongchao Shi, Jianping Fan, Zhiqiang He
2022 arXiv   pre-print
segmentation) as well as multiple sensory data stream (images, point clouds, and vision-language data).  ...  to organize the representative methods according to their motivations, structures, and application scenarios.  ...  SETR demonstrates the feasibility of the visual Transformer for the segmentation tasks, but it also brings unacceptably extra GPU costs. TransUNet [96] is the first for medical image segmentation.  ... 
arXiv:2111.06091v3 fatcat:a3fq6lvvzzgglb3qtus5qwrwpe

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.  ...  Shortly after deep learning algorithms were applied to Image Analysis, and more importantly to medical imaging, their applications increased significantly to become a trend.  ...  self-paced learning (DASL) strategy for reducing annotation effort and making use of un-annotated samples(weekly supervised).  ... 
doi:10.1007/s00138-020-01101-5 pmid:32834523 pmcid:PMC7386599 fatcat:tkkylrptc5hkpoj52hjs3kuttu

Working memory inspired hierarchical video decomposition with transformative representations [article]

Binjie Qin, Haohao Mao, Ruipeng Zhang, Yueqi Zhu, Song Ding, Xu Chen
2022 arXiv   pre-print
Video decomposition is very important to extract moving foreground objects from complex backgrounds in computer vision, machine learning, and medical imaging, e.g., extracting moving contrast-filled vessels  ...  backprojection module embody unstructured random representations of the control layer in working memory, recurrently projecting spatiotemporally decomposed nonlocal patches into orthogonal subspaces for  ...  ACKNOWLEDGMENTS The authors would like to thank all the cited authors for providing the source codes used in this work and the anonymous reviewers for their valuable comments on the manuscript.  ... 
arXiv:2204.10105v3 fatcat:ifzpeay2qjfvbaznwruwc4dz5m

A combined local and global motion estimation and compensation method for cardiac CT

Qiulin Tang, Beshan Chiang, Akinola Akinyemi, Alexander Zamyatin, Bibo Shi, Satoru Nakanishi, Bruce R. Whiting, Christoph Hoeschen
2014 Medical Imaging 2014: Physics of Medical Imaging  
It is based on an articulated atlas for CT images that learned the shape and appearance of the individual bones along with the articulation between them from annotated training instances.  ...  For evaluation, a head&neck bone atlas created from 15 manually annotated training images was adapted to 58 clinically acquired head&neck CT datasets.  ...  FTIR chemical imaging allows the spatial distribution of chemistry to be rapidly imaged at a high (diffraction limited) spatial resolution where a pixel represents an area of 5.5 ? 5.5 µm2 of tissue.  ... 
doi:10.1117/12.2043492 fatcat:fyzpc5m6jbh7fjohqpdmtzkhte

A Survey of Uncertainty in Deep Neural Networks [article]

Jakob Gawlikowski, Cedrique Rovile Njieutcheu Tassi, Mohsin Ali, Jongseok Lee, Matthias Humt, Jianxiang Feng, Anna Kruspe, Rudolph Triebel, Peter Jung, Ribana Roscher, Muhammad Shahzad, Wen Yang (+2 others)
2022 arXiv   pre-print
For a practical application, we discuss different measures of uncertainty, approaches for the calibration of neural networks and give an overview of existing baselines and implementations.  ...  Additionally, the practical limitations of current methods for mission- and safety-critical real world applications are discussed and an outlook on the next steps towards a broader usage of such methods  ...  based graph convolutional networks for organ segmentation refine- Willke, “Out-of-distribution detection using an ensemble of self super- ment,” in Medical Imaging with Deep Learning.  ... 
arXiv:2107.03342v3 fatcat:cex5j3xq5fdijjdtdbt2ixralm

Where am I? Creating spatial awareness in unmanned ground robots using SLAM: A survey

NITIN KUMAR DHIMAN, DIPTI DEODHARE, DEEPAK KHEMANI
2015 Sadhana (Bangalore)  
This paper presents a survey of Simultaneous Localization And Mapping (SLAM) algorithms for unmanned ground robots.  ...  SLAM algorithms for both static and dynamic environments have been surveyed. The algorithms in each class are further divided based on the techniques used.  ...  For finding overlap between local maps, an approach based on robust point feature matching of medical image registration method is used.  ... 
doi:10.1007/s12046-015-0402-6 fatcat:33hu7lxwsja2fdjepsdxjczd54

Welcome Messages

2019 2019 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)  
The database along with its source code will be made open source for the research and academic purpose.  ...  The application of the H-BTC images range from image compression and retrieval, indexing, reconstruction, classification and so on.  ...  An efficient method is to generate pseudo-annotations from manual bounding boxes, and then uses the pseudoannotations to train the segmentation model [4] .  ... 
doi:10.1109/ispacs48206.2019.8986291 fatcat:gu4zaxsqkncp5n2ebj5fybk7ce

2022 Review of Data-Driven Plasma Science [article]

Rushil Anirudh, Rick Archibald, M. Salman Asif, Markus M. Becker, Sadruddin Benkadda, Peer-Timo Bremer, Rick H.S. Budé, C.S. Chang, Lei Chen, R. M. Churchill, Jonathan Citrin, Jim A Gaffney (+51 others)
2022 arXiv   pre-print
Therefore, it is imperative to train machines to analyze and interpret (eventually) such data as intelligently as humans but far more efficiently in quantity.  ...  It is now becoming impractical for humans to analyze all the data manually.  ...  A canonical imaging system can be represented as y = Ax + e, (1) where y ∈ R m represents sensor measurements, x ∈ R n represents the unkown image, A represents an imaging operator, and η denotes noise  ... 
arXiv:2205.15832v1 fatcat:fxsl6gl3fncnhpoj76defxoc3a

Power pulsing of the CALICE tile hadron calorimeter

Mathias Reinecke
2016 2016 IEEE Nuclear Science Symposium, Medical Imaging Conference and Room-Temperature Semiconductor Detector Workshop (NSS/MIC/RTSD)  
This achievement was recognised through the award of the 2015 Nobel prize for physics to the leaders of the SNO and Super-Kamiokande experiments for the conclusive establishment of the phenomenon of neutrino  ...  For example, we now know that neutrinos have a mass, providing clear evidence for physics beyond the our current understanding.  ...  The contents are solely the responsibility of the authors and do not represent the official views of the NIBIB.  ... 
doi:10.1109/nssmic.2016.8069748 fatcat:zjgd7dmfdbhntb4kfwdtrlejhi

25th Annual Computational Neuroscience Meeting: CNS-2016

Tatyana O. Sharpee, Alain Destexhe, Mitsuo Kawato, Vladislav Sekulić, Frances K. Skinner, Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári, Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett (+597 others)
2016 BMC Neuroscience  
BMC Neuroscience 2016, 17(Suppl 1):A1 Neural circuits are notorious for the complexity of their organization.  ...  These results outline a framework for categorizing neuronal types based on their functional properties.  ...  Allen and Jody Allen, for their vision, encouragement and support.  ... 
doi:10.1186/s12868-016-0283-6 pmid:27534393 pmcid:PMC5001212 fatcat:bt45etzj2bbolfcxlxo7hlv6ju

Pretrained Transformers for Text Ranking: BERT and Beyond [article]

Jimmy Lin, Rodrigo Nogueira, Andrew Yates
2021 arXiv   pre-print
The combination of transformers and self-supervised pretraining has been responsible for a paradigm shift in natural language processing (NLP), information retrieval (IR), and beyond.  ...  Although transformer architectures and pretraining techniques are recent innovations, many aspects of how they are applied to text ranking are relatively well understood and represent mature techniques  ...  Special thanks goes out to two anonymous reviewers for their insightful comments and helpful feedback.  ... 
arXiv:2010.06467v3 fatcat:obla6reejzemvlqhvgvj77fgoy
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