AI in Medical Imaging Informatics: Current Challenges and Future Directions

Panayides, Alexandros; Amini, Amir A.; Filipović, Nenad; Sharma, Ashish; Tsaftaris, Sotirios A.; Young, Alistair A.; Foran, David J.; Do, Nhan; Golemati, Spyretta; Kurç, Tahsin; Huang, Kun; Nikita, Konstantina S.; Veasey, Ben P.; Zervakis, Michalis; Saltz, Joel; Pattichis, Constantinos S.

doi:10.1109/jbhi.2020.2991043

Cited by 248 publications

(103 citation statements)

References 238 publications

(242 reference statements)

Supporting

Mentioning

101

Contrasting

Order By: Relevance

“…In future work, we will further investigate the potential of our proposed method in seismic data reconstruction [49], [50], and seismic facies analysis [51]. Meanwhile, some follow-up works can apply the TSC-SAC model to video processing [5]- [7], and medical image processing [8]- [10].…”

Section: Discussionmentioning

confidence: 99%

“…Sparse coding (SC) is an unsupervised method to learn the useful structure or pattern of real data, which is widely used in signal denoising [1], [2], natural image denoising [3], [4], video denoising [5], [6] [7], and medical image denoising [8], [9] [10]. With the increasing demand for highquality subsurface images, SC is naturally applied in seismic data denoising [11], [12].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

3-D Seismic Noise Attenuation via Tensor Sparse Coding With Spatially Adaptive Coherence Constraint

Qian

Fan

et al. 2021

IEEE Access

View full text Add to dashboard Cite

Tensor sparse coding (TSC) is a method used to excavate 3D volume structures extended by sparse coding (SC), which is increasingly applied in data noise attenuation. Existing TSC approaches control the intensity of noise attenuation by using a predetermined soft or hard threshold that relies on the noise variance. However, the noise variance in seismic data is unknown and varies with time and space, leading to the conventional TSC method not being able to track this change. To address this issue, we proposed a tensor sparse coding model with spatially adaptive coherent constraint (TSC-SAC) to find an optimal adjustable threshold without the demand for prior knowledge of the noise variance. The threshold is determined by the coherence of the residual with respect to the dictionary. Moreover, a tensor spatial coherence orthogonal matching pursuit algorithm (TSC-OMP) is developed for solving sparse representation. Unlike the existing threshold strategy in traditional TSC methods, TSC-OMP utilizes an ideal spatially adaptive coherence threshold to regulate the sparsity, which can effectively preserve the valuable information in processing for noise suppression. By comparing with four state-of-the-art denoising algorithms, we then demonstrated the superior performance of TSC-SAC on both a synthetic and two field data sets.INDEX TERMS Tensor sparse coding, spatially adaptive coherence threshold, tensor spatial coherence orthogonal matching pursuit (TSC-OMP).

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

3-D Seismic Noise Attenuation via Tensor Sparse Coding With Spatially Adaptive Coherence Constraint

Qian

Fan

et al. 2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Future work will firstly focus in the evaluation of the platform in a more wide spectrum of imaging cases. Moreover, it is targeted to integrate in the platform the visualization of quantitative imaging analytics through the construction of imaging signatures (i.e., fusing shape, texture, morphology, intensity, etc., features) and their subsequent association to clinical outcomes [24].…”

Section: Discussionmentioning

confidence: 99%

A Multi-User Virtual Reality Application For Visualization And Analysis In Medical Imaging

Prodromou

Leandrou

Schiza

et al. 2020

2020 IEEE 20th International Conference on Bioinformatics and Bioengineering (BIBE)

View full text Add to dashboard Cite

3D medical imaging provides an invaluable tool to the radiologist in visualizing normal and abnormal tissue and structure for the assessment of disease and treatment planning. Moreover, in difficult image disease assessment cases, as well as in the assessment of the early stages of the disease the need exists for a real time 3D collaborative platform. The objective of this paper was to develop a multi-user Virtual Reality (VR) application for visualization and analysis in medical imaging. The proposed platform is based on the Unity VR platform that is integrated with the very well-known and popular image Visualization Toolkit-VTK. The platform was evaluated successfully in the 3D visualization of images of the ADNI dataset. Future work will focus in integrating in the platform the visualization of quantitative imaging analytics as well as the evaluation of the platform in a more-wide spectrum of imaging cases.

show abstract

“…These keywords are focused on the analysis of medical images. Digital medicine can change the imaging informatics known in the medical world today, thereby providing more accurate, timely and effective treatment plans [ 109 ]. As of 2020, the artificial intelligence medical imaging market is the second largest market segment in artificial intelligence medical applications, second only to drug research and development, accounting for 35%.…”

Section: Bibliometric Analysis Of Digital Medical Researchmentioning

confidence: 99%

Integrating Digital Technologies and Public Health to Fight Covid-19 Pandemic: Key Technologies, Applications, Challenges and Outlook of Digital Healthcare

Wang

Zhang

et al. 2021

IJERPH

101

View full text Add to dashboard Cite

Integration of digital technologies and public health (or digital healthcare) helps us to fight the Coronavirus Disease 2019 (COVID-19) pandemic, which is the biggest public health crisis humanity has faced since the 1918 Influenza Pandemic. In order to better understand the digital healthcare, this work conducted a systematic and comprehensive review of digital healthcare, with the purpose of helping us combat the COVID-19 pandemic. This paper covers the background information and research overview of digital healthcare, summarizes its applications and challenges in the COVID-19 pandemic, and finally puts forward the prospects of digital healthcare. First, main concepts, key development processes, and common application scenarios of integrating digital technologies and digital healthcare were offered in the part of background information. Second, the bibliometric techniques were used to analyze the research output, geographic distribution, discipline distribution, collaboration network, and hot topics of digital healthcare before and after COVID-19 pandemic. We found that the COVID-19 pandemic has greatly accelerated research on the integration of digital technologies and healthcare. Third, application cases of China, EU and U.S using digital technologies to fight the COVID-19 pandemic were collected and analyzed. Among these digital technologies, big data, artificial intelligence, cloud computing, 5G are most effective weapons to combat the COVID-19 pandemic. Applications cases show that these technologies play an irreplaceable role in controlling the spread of the COVID-19. By comparing the application cases in these three regions, we contend that the key to China’s success in avoiding the second wave of COVID-19 pandemic is to integrate digital technologies and public health on a large scale without hesitation. Fourth, the application challenges of digital technologies in the public health field are summarized. These challenges mainly come from four aspects: data delays, data fragmentation, privacy security, and data security vulnerabilities. Finally, this study provides the future application prospects of digital healthcare. In addition, we also provide policy recommendations for other countries that use digital technology to combat COVID-19.

show abstract

AI in Medical Imaging Informatics: Current Challenges and Future Directions

Cited by 248 publications

References 238 publications

3-D Seismic Noise Attenuation via Tensor Sparse Coding With Spatially Adaptive Coherence Constraint

3-D Seismic Noise Attenuation via Tensor Sparse Coding With Spatially Adaptive Coherence Constraint

A Multi-User Virtual Reality Application For Visualization And Analysis In Medical Imaging

Integrating Digital Technologies and Public Health to Fight Covid-19 Pandemic: Key Technologies, Applications, Challenges and Outlook of Digital Healthcare

Contact Info

Product

Resources

About