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Guest Editorial Annotation-Efficient Deep Learning: The Holy Grail of Medical Imaging

Nima Tajbakhsh, Holger Roth, Demetri Terzopoulos, Jianming Liang
2021 IEEE Transactions on Medical Imaging  
For the task of semantic segmentation, compiling large-scale (nonsynthetic) medical image datasets with pixel-level annotations is time-consuming and often prohibitively expensive, and it may be impossible  ...  Annotations can be acquired at the patient, image, and pixel levels.  ... 
doi:10.1109/tmi.2021.3089292 pmid:34795461 pmcid:PMC8594751 fatcat:t7kufjbdyfgazng3gcuyuhawxu

Transformers in Medical Image Analysis: A Review [article]

Kelei He, Chen Gan, Zhuoyuan Li, Islem Rekik, Zihao Yin, Wen Ji, Yang Gao, Qian Wang, Junfeng Zhang, Dinggang Shen
2022 arXiv   pre-print
Second, we review various Transformer architectures tailored for medical image applications and discuss their limitations.  ...  Our paper aims to promote awareness and application of Transformers in the field of medical image analysis.  ...  ACKNOWLEDGEMENT This work was supported in part by the National Nature Science Foundation of China under grant No. 62106101.  ... 
arXiv:2202.12165v3 fatcat:a2bur66wxrbvtjy7wswzhohglm

Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models [article]

Jialin Peng, Ye Wang
2021 arXiv   pre-print
The labeling costs for medical images are very high, especially in medical image segmentation, which typically requires intensive pixel/voxel-wise labeling.  ...  Therefore, the strong capability of learning and generalizing from limited supervision, including a limited amount of annotations, sparse annotations, and inaccurate annotations, is crucial for the successful  ...  A similar idea has been adopted in [185] to weakly supervised segmentation of covid-19 in CT images. For semi-supervised medical image segmentation, Peng et al.  ... 
arXiv:2103.00429v1 fatcat:p44a5e34sre4nasea5kjvva55e

Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models

Jialin Peng, Ye Wang
2021 IEEE Access  
The labeling costs for medical images are very high, especially in medical image segmentation, which typically requires intensive pixel/voxel-wise labeling.  ...  Therefore, the strong capability of learning and generalizing from limited supervision, including a limited amount of annotations, sparse annotations, and inaccurate annotations, is crucial for the successful  ...  segmentation [22] , and segmentation for covid-19 [13] , the surveys by [16] , [23] , [24] review advancement of deep network architectures, losses, and training strategies for medical image segmentation  ... 
doi:10.1109/access.2021.3062380 fatcat:r5vsec2yfzcy5nk7wusiftyayu

Cross-Domain Segmentation with Adversarial Loss and Covariate Shift for Biomedical Imaging [article]

Bora Baydar, Savas Ozkan, A. Emre Kavur, N. Sinem Gezer, M. Alper Selver, Gozde Bozdagi Akar
2020 arXiv   pre-print
Experiments are also conducted on Covid-19 dataset that it consists of CT data where significant intra-class visual differences are observed.  ...  Despite the widespread use of deep learning methods for semantic segmentation of images that are acquired from a single source, clinicians often use multi-domain data for a detailed analysis.  ...  ACKNOWLEDGMENT The authors gratefully acknowledge the support of NVIDIA Corporation with the donation of GPUs used for this research.  ... 
arXiv:2006.04390v1 fatcat:b64ze2433rh7lpbkm6gmmp5xuq

A survey on attention mechanisms for medical applications: are we moving towards better algorithms? [article]

Tiago Gonçalves, Isabel Rio-Torto, Luís F. Teixeira, Jaime S. Cardoso
2022 arXiv   pre-print
In healthcare, there is a strong need for tools that may improve the routines of the clinicians and the patients.  ...  The increasing popularity of attention mechanisms in deep learning algorithms for computer vision and natural language processing made these models attractive to other research domains.  ...  the Operational Program for Competitiveness and Internationalisation -COMPETE2020, the North Portugal Regional Operational Program -NORTE 2020 and by the Portuguese Foundation for Science and Technology  ... 
arXiv:2204.12406v1 fatcat:lwz3hvd44bfqnhf7n57ejehidu

Unsupervised domain adaptation for clinician pose estimation and instance segmentation in the operating room [article]

Vinkle Srivastav, Afshin Gangi, Nicolas Padoy
2022 arXiv   pre-print
Computer vision models for person pixel-based segmentation and body-keypoints detection are needed to better understand the clinical activities and the spatial layout of the OR.  ...  Second, to address the domain shift and the lack of annotations, we propose a novel unsupervised domain adaptation method, called AdaptOR, to adapt a model from an in-the-wild labeled source domain to  ...  The federated learning has been recently used in medical imaging for segmenting the brain tumor (Sheller et al., 2018) and detecting COVID-19 lung abnormalities in CT (Dou et al., 2021) .  ... 
arXiv:2108.11801v4 fatcat:4fxsoswfpvfejbcqmzvkdorpky

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)  
- Aided Diagnosis of COVID-19 on CT Images DAY 2 -Jan 13, 2021 Rosas-Arias, Leonel; Benitez- Garcia, Gibran; Portillo-Portillo, Jose; Sanchez-Perez, Gabriel; Yanai, Keiji 738 Fast and Accurate  ...  Prior Information of the Driving Environment DAY 2 -Jan 13, 2021 Mitsuno, Kakeru; Nomura, Yuichiro; Kurita, Takio 2083 Channel Planting for Deep Neural Networks Using Knowledge Distillation  ... 
doi:10.1109/icpr48806.2021.9412725 fatcat:3vge2tpd2zf7jcv5btcixnaikm

Learning with Capsules: A Survey [article]

Fabio De Sousa Ribeiro, Kevin Duarte, Miles Everett, Georgios Leontidis, Mubarak Shah
2022 arXiv   pre-print
However, a major hurdle for capsule network research has been the lack of a reliable point of reference for understanding their foundational ideas and motivations.  ...  The aim of this survey is to provide a comprehensive overview of the capsule network research landscape, which will serve as a valuable resource for the community going forward.  ...  ACKNOWLEDGMENTS The authors would like to thank all reviewers, and especially Professor Chris Williams from the School of Informatics of the University of Edinburgh, who provided constructive feedback  ... 
arXiv:2206.02664v1 fatcat:auiy6oo5tbfghkppfyxysjiyty

Learning Disentangled Representations in the Imaging Domain [article]

Xiao Liu, Pedro Sanchez, Spyridon Thermos, Alison Q. O'Neil, Sotirios A. Tsaftaris
2022 arXiv   pre-print
This alleviation of the data and annotation requirements offers tantalising prospects for applications in computer vision and healthcare.  ...  In this tutorial paper, we motivate the need for disentangled representations, revisit key concepts, and describe practical building blocks and criteria for learning such representations.  ...  We thank the participants of the DREAM tutorials for feedback.  ... 
arXiv:2108.12043v5 fatcat:cbpmp6pbajhjvjzovulswuj2wy


Dr. Agusthiyar . R
2022 Zenodo  
The dynamism of the young talent blooming in our garden is being tapped; the skills and the potentialities of its students and faculty members are being mined out and chiseled.  ...  SRM IST Ramapuram Campus stands tall for its successful, developed holistic system where the formal education has been intricately woven with moral, spiritual and social education.  ...  First, this article looked at a number of studies on the COVID 19 epidemic. The essential principle of the deep CNN model was then described.  ... 
doi:10.5281/zenodo.7024997 fatcat:2v4vxqlfxbgn3dl43zr4y2d26y

Medical machine intelligence: swarm optimization, feature fusion, and neighboring-awareness

Siyuan Lu
The fusion of multiple features and classifiers is effective for COVID-19 diagnosis, but the context information within a single feature set can be further explored. The classification perf [...]  ...  Specifically, the contributions to the detection of COVID-19 using chest CT scans are demonstrated.  ...  Acknowledgments It still feels like a dream for me to study in the UK in pursuit of PhD because I am a seriously  ... 
doi:10.25392/ fatcat:gxcccxrcyvc3jco5ieaivcozwq

Scalable Instance Segmentation for Microscopy

Constantin Pape
This efficient algorithm can segment images directly from pixels without the need for seeds or thresholds.  ...  A key component is the instance segmentation of structures of interest, such as cells, neurons or organelles. In this thesis, we develop scalable methods for boundary based instance segmentation.  ...  Figure 6 .6 panel (A) provides an overview of these steps, panel (B) shows example IF images for a COVID-19 patient and a healthy donor.  ... 
doi:10.11588/heidok.00030147 fatcat:g5aldql5jzhcblqptrgaafcrhq

WILDS: A Benchmark of in-the-Wild Distribution Shifts [article]

Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, Tony Lee, Etienne David (+11 others)
2021 arXiv   pre-print
for tumor identification; across camera traps for wildlife monitoring; and across time and location in satellite imaging and poverty mapping.  ...  This gap remains even with models trained by existing methods for tackling distribution shifts, underscoring the need for new methods for training models that are more robust to the types of distribution  ...  We are grateful for all of the helpful suggestions and constructive feedback from: Aditya Khosla, Andreas Schlueter, Annie Chen, Aleksander Madry, Alexander D'Amour, Allison Koenecke We also gratefully  ... 
arXiv:2012.07421v3 fatcat:bsohmukpszajxeadeo25oxmbs4

Algorithmic Fairness Datasets: the Story so Far [article]

Alessandro Fabris, Stefano Messina, Gianmaria Silvello, Gian Antonio Susto
2022 arXiv   pre-print
We discuss different approaches and levels of attention to these topics, making them tangible, and distill them into a set of best practices for the curation of novel resources.  ...  Secondly, we document and summarize hundreds of available alternatives, annotating their domain and supported fairness tasks, along with additional properties of interest for fairness researchers.  ...  Acknowledgements The authors would like to thank the following researchers and dataset creators for the useful feedback on the data briefs: Alain Barrat, Luc Behaghel, Asia Biega, Marko Bohanec, Chris  ... 
arXiv:2202.01711v3 fatcat:kd546yklwjhvtkrbhtzgbzb2xm
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