Convolutional Network With Twofold Feature Augmentation for Diabetic Retinopathy Recognition From Multi-Modal Images

Hua, Cam-Hao; Kim, Ki‐Young; Huynh‐The, Thien; You, Jong In; Yu, Seung-Young; Le-Tien, Thuong; Bae, Sung Ho; Lee, Sungyoung

doi:10.1109/jbhi.2020.3041848

Cited by 40 publications

(15 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…With the great success of DL, especially CNN architectures, in the image processing and computer vision domains, few recent years have witnessed a vast emergence of DL to address various challenging tasks of medical image analysis because of the requirement of much more specialized knowledge from technicians and medical experts if compared with natural image analysis [142], [143]. For lesion segmentation in breast ultrasound (BUS) images, the work [144] studied an advanced network, namely saliency-guided morphology-aware U-Net (SMU-Net), by involving an additional middle feature learning stream and an auxiliary network.…”

Section: A Healthcarementioning

confidence: 99%

Artificial Intelligence for the Metaverse: A Survey

Huynh‐The¹,

Pham²,

Pham³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Along with the massive growth of the Internet from the 1990s until now, various innovative technologies have been created to bring users breathtaking experiences with more virtual interactions in cyberspace. Many virtual environments with thousands of services and applications, from social networks to virtual gaming worlds, have been developed with immersive experience and digital transformation, but most are incoherent instead of being integrated into a platform. In this context, metaverse, a term formed by combining meta and universe, has been introduced as a shared virtual world that is fueled by many emerging technologies, such as fifth-generation networks and beyond, virtual reality, and artificial intelligence (AI). Among such technologies, AI has shown the great importance of processing big data to enhance immersive experience and enable humanlike intelligence of virtual agents. In this survey, we make a beneficial effort to explore the role of AI in the foundation and development of the metaverse. We first deliver a preliminary of AI, including machine learning algorithms and deep learning architectures, and its role in the metaverse. We then convey a comprehensive investigation of AI-based methods concerning six technical aspects that have potentials for the metaverse: natural language processing, machine vision, blockchain, networking, digital twin, and neural interface, and being potential for the metaverse. Subsequently, several AI-aided applications, such as healthcare, manufacturing, smart cities, and gaming, are studied to be deployed in the virtual worlds. Finally, we conclude the key contribution of this survey and open some future research directions in AI for the metaverse.

show abstract

Section: A Healthcarementioning

confidence: 99%

Artificial Intelligence for the Metaverse: A Survey

Huynh‐The¹,

Pham²,

Pham³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Subsequently, the fused feature vectors were passed into an SVM classifier to predict activities. A comprehensive diabetic retinopathy recognition method [121] technology with CNN architectures to learn the amalgamation between fundus images and wide-field swept-source optical coherence tomography angiography. In this method, a twofold feature augmentation mechanism was advanced to enrich the generalization capacity of the feature level and prevent CNN from the vanishing gradient problem.…”

Section: G Other Technical Aspectsmentioning

confidence: 99%

Applications of Explainable AI for 6G: Technical Aspects, Use Cases, and Research Challenges

Wang¹,

Qureshi²,

Miralles-Pechuaán³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

When 5G began its commercialisation journey around 2020, the discussion on the vision of 6G also surfaced. Researchers expect 6G to have higher bandwidth, coverage, reliability, energy efficiency, lower latency, and, more importantly, an integrated "human-centric" network system powered by artificial intelligence (AI). Such a 6G network will lead to an excessive number of automated decisions made every second. These decisions can range widely, from network resource allocation to collision avoidance for self-driving cars. However, the risk of losing control over decision-making may increase due to high-speed data-intensive AI decision-making beyond designers and users' comprehension. The promising explainable AI (XAI) methods can mitigate such risks by enhancing the transparency of the black box AI decision-making process. This survey paper highlights the need for XAI towards the upcoming 6G age in every aspect, including 6G technologies (e.g., intelligent radio, zero-touch network management) and 6G use cases (e.g., industry 5.0). Moreover, we summarised the lessons learned from the recent attempts and outlined important research challenges in applying XAI for building 6G systems. This research aligns with goals 9, 11, 16, and 17 of the United Nations Sustainable Development Goals (UN-SDG) 1 , promoting innovation and building infrastructure, sustainable and inclusive human settlement, advancing justice and strong institutions, and fostering partnership at the global level.

show abstract

“…D UE to the safety and cost-effectiveness in acquiring, fundus images are widely used by ophthalmologists for early eye disease detection and diagnosis, including glaucoma [3]- [5], diabetic retinopathy [6]- [8], cataract [9], [10], and age-This work was supported in part by the Shenzhen Bay Laboratory and the Shenzhen International Science and Technology Information Center.…”

Section: Introductionmentioning

confidence: 99%