Transformers in medical imaging: A survey

Medical Image Analysis, Journal Year: 2023, Volume and Issue: 88, P. 102802 - 102802

Published: April 5, 2023

Language: Английский

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Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications

IEEE Journal on Selected Areas in Communications, Journal Year: 2021, Volume and Issue: 40(1), P. 5 - 36

Published: Nov. 8, 2021

The thriving of artificial intelligence (AI) applications is driving the further evolution wireless networks. It has been envisioned that 6G will be transformative and revolutionize from "connected things" to intelligence". However, state-of-the-art deep learning big data analytics based AI systems require tremendous computation communication resources, causing significant latency, energy consumption, network congestion, privacy leakage in both training inference processes. By embedding model capabilities into edge, edge stands out as a disruptive technology for seamlessly integrate sensing, communication, computation, intelligence, thereby improving efficiency, effectiveness, privacy, security In this paper, we shall provide our vision scalable trustworthy with integrated design strategies decentralized machine models. New principles networks, service-driven resource allocation optimization methods, well holistic end-to-end system architecture support described. Standardization, software hardware platforms, application scenarios are also discussed facilitate industrialization commercialization systems.

Language: Английский

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Machine learning for medical imaging: methodological failures and recommendations for the future

npj Digital Medicine, Journal Year: 2022, Volume and Issue: 5(1)

Published: April 12, 2022

Research in computer analysis of medical images bears many promises to improve patients' health. However, a number systematic challenges are slowing down the progress field, from limitations data, such as biases, research incentives, optimizing for publication. In this paper we review roadblocks developing and assessing methods. Building our on evidence literature data challenges, show that at every step, potential biases can creep in. On positive note, also discuss on-going efforts counteract these problems. Finally provide recommendations how further address problems future.

Language: Английский

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AbdomenCT-1K: Is Abdominal Organ Segmentation a Solved Problem?

IEEE Transactions on Pattern Analysis and Machine Intelligence, Journal Year: 2021, Volume and Issue: 44(10), P. 6695 - 6714

Published: July 27, 2021

With the unprecedented developments in deep learning, automatic segmentation of main abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have achieved comparable results with inter-rater variability on many benchmark datasets. However, most existing datasets only contain single-center, single-phase, single-vendor, or single-disease cases, and it is unclear whether excellent performance can generalize diverse This paper presents large CT organ dataset, termed AbdomenCT-1K, more than 1000 (1K) scans from 12 medical centers, including multi-phase, multi-vendor, multi-disease cases. Furthermore, we conduct large-scale study for liver, kidney, spleen, pancreas reveal unsolved problems SOTA methods, such limited generalization ability distinct phases, unseen diseases. To advance problems, further build four benchmarks fully supervised, semi-supervised, weakly continual which are currently challenging active research topics. Accordingly, develop simple effective method each benchmark, used out-of-the-box strong baselines. We believe AbdomenCT-1K dataset will promote future in-depth towards clinical applicable methods.

Language: Английский

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A Study of CNN and Transfer Learning in Medical Imaging: Advantages, Challenges, Future Scope

Sustainability, Journal Year: 2023, Volume and Issue: 15(7), P. 5930 - 5930

Published: March 29, 2023

This paper presents a comprehensive study of Convolutional Neural Networks (CNN) and transfer learning in the context medical imaging. Medical imaging plays critical role diagnosis treatment diseases, CNN-based models have demonstrated significant improvements image analysis classification tasks. Transfer learning, which involves reusing pre-trained CNN models, has also shown promise addressing challenges related to small datasets limited computational resources. reviews advantages imaging, including improved accuracy, reduced time resource requirements, ability address class imbalances. It discusses challenges, such as need for large diverse datasets, interpretability deep models. What factors contribute success these networks? How are they fashioned, exactly? motivated them build structures that did? Finally, current future research directions opportunities, development specialized architectures exploration new modalities applications using techniques. Overall, highlights potential field while acknowledging continued overcome existing limitations.

Language: Английский

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Shallow Attention Network for Polyp Segmentation

Lecture notes in computer science, Journal Year: 2021, Volume and Issue: unknown, P. 699 - 708

Published: Jan. 1, 2021

Language: Английский

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Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives

Medical Image Analysis, Journal Year: 2023, Volume and Issue: 85, P. 102762 - 102762

Published: Jan. 31, 2023

Language: Английский

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Self-supervised learning methods and applications in medical imaging analysis: a survey

PeerJ Computer Science, Journal Year: 2022, Volume and Issue: 8, P. e1045 - e1045

Published: July 19, 2022

The scarcity of high-quality annotated medical imaging datasets is a major problem that collides with machine learning applications in the field analysis and impedes its advancement. Self-supervised recent training paradigm enables robust representations without need for human annotation which can be considered an effective solution data. This article reviews state-of-the-art research directions self-supervised approaches image data concentration on their analysis. covers set most methods from computer vision as they are applicable to categorize them predictive, generative, contrastive approaches. Moreover, 40 papers aiming at shedding light innovation field. Finally, concludes possible future

Language: Английский

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Deep reinforcement learning in medical imaging: A literature review

Medical Image Analysis, Journal Year: 2021, Volume and Issue: 73, P. 102193 - 102193

Published: July 27, 2021

Language: Английский

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Vision Transformers in medical computer vision—A contemplative retrospection

Engineering Applications of Artificial Intelligence, Journal Year: 2023, Volume and Issue: 122, P. 106126 - 106126

Published: March 20, 2023

Language: Английский

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