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Deep learning-based recognition and segmentation of intracranial aneurysms under small sample size

Guangyu Zhu, Xueqi Luo, Tingting Yang, Li Cai et al. • Frontiers in Physiology (2022)

The manual identification and segmentation of intracranial aneurysms (IAs) involved in the 3D reconstruction procedure are labor-intensive and prone to human errors. To meet the demands for routine clinical management and large cohort studi...

DOAJ Open Access (DOAJ) DOI: 10.3389/fphys.2022.1084202

A Survey of Deep Learning-Based Object Detection

Licheng Jiao, Fan Zhang, Fang Liu, Shuyuan Yang et al. • IEEE Access (2019)

Object detection is one of the most important and challenging branches of computer vision, which has been widely applied in people’s life, such as monitoring security, autonomous driving and so on, with the purpose of locating instances of ...

OPENALEX cc-by DOI: https://doi.org/10.1109/access.2019.2939201

Deep learning microscopy

Yair Rivenson, Zoltán S. Göröcs, Harun Günaydın, Yibo Zhang et al. • Optica (2017)

We demonstrate that a deep neural network can significantly improve optical microscopy, enhancing its spatial resolution over a large field-of-view and depth-of-field. After its training, the only input to this network is an image acquired ...

OPENALEX Open Access DOI: https://doi.org/10.1364/optica.4.001437

Deep Learning: An Update for Radiologists

Phillip M. Cheng, Emmanuel Montagnon, Rikiya Yamashita, Ian Pan et al. • Radiographics (2021)

Deep learning is a class of machine learning methods that has been successful in computer vision. Unlike traditional machine learning methods that require hand-engineered feature extraction from input images, deep learning methods learn the...

OPENALEX cc-by DOI: https://doi.org/10.1148/rg.2021200210

Vison transformer adapter-based hyperbolic embeddings for multi-lesion segmentation in diabetic retinopathy

Zijian Wang, Haimei Lu, Haixin Yan, Hongxing Kan et al. • Scientific Reports (2023)

Abstract Diabetic Retinopathy (DR) is a major cause of blindness worldwide. Early detection and treatment are crucial to prevent vision loss, making accurate and timely diagnosis critical. Deep learning technology has shown promise in the a...

DOAJ Open Access (DOAJ) DOI: 10.1038/s41598-023-38320-5

MalariaSED: a deep learning framework to decipher the regulatory contributions of noncoding variants in malaria parasites

Chengqi Wang, Yibo Dong, Chang Li, Jenna Oberstaller et al. • Genome Biology (2023)

Abstract Malaria remains one of the deadliest infectious diseases. Transcriptional regulation effects of noncoding variants in this unusual genome of malaria parasites remain elusive. We developed a sequence-based, ab initio deep learning f...

DOAJ Open Access (DOAJ) DOI: 10.1186/s13059-023-03063-z

Deep Learning for Diabetes: A Systematic Review

Taiyu Zhu, Kezhi Li, Pau Herrero, Pantelis Georgiou • IEEE Journal of Biomedical and Health Informatics (2020)

Diabetes is a chronic metabolic disorder that affects an estimated 463 million people worldwide. Aiming to improve the treatment of people with diabetes, digital health has been widely adopted in recent years and generated a huge amount of ...

OPENALEX other-oa DOI: https://doi.org/10.1109/jbhi.2020.3040225

Deep learning with COTS HPC systems

Adam Coates, Brody Huval, Tao Wang, David J. Wu et al. (2013)

Scaling up deep learning algorithms has been shown to lead to increased performance in benchmark tasks and to enable discovery of complex high-level features. Recent efforts to train extremely large networks (with over 1 billion parameters)...

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A learning‐based energy management strategy for hybrid energy storage systems with compressed air and solid oxide fuel cells

Yundie Guan, Xiangyu Zhang, Zheming Liang, Tao Chen • IET Renewable Power Generation (2025)

Abstract The intermittency and volatility of renewable energy have been major challenges in modern power systems. This paper proposes a self‐adaptive energy management strategy based on deep reinforcement learning (DRL) to integrate renewab...

DOAJ Open Access (DOAJ) DOI: 10.1049/rpg2.13192

Jet-images — deep learning edition

Luke de Oliveira • OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (2022)

Building on the notion of a particle physics detector as a camera and the collimated streams of high energy particles, or jets, it measures as an image, we investigate the potential of machine learning techniques based on deep learning arch...

OPENALEX cc-by DOI: https://doi.org/10.1007/jhep07%282016%29069

Jet-images — deep learning edition

Luke de Oliveira, Michael Kagan, Lester W. Mackey, Benjamin Philip Nachman et al. • Journal of High Energy Physics (2016)

Building on the notion of a particle physics detector as a camera and the collimated streams of high energy particles, or jets, it measures as an image, we investigate the potential of machine learning techniques based on deep learning arch...

OPENALEX cc-by DOI: https://doi.org/10.1007/jhep07(2016)069

Deep learning for electroencephalogram (EEG) classification tasks: a review

Alexander Craik, Yongtian He, Jose Luis Contreras-Vidal • Journal of Neural Engineering (2019)

OBJECTIVE: Electroencephalography (EEG) analysis has been an important tool in neuroscience with applications in neuroscience, neural engineering (e.g. Brain-computer interfaces, BCI's), and even commercial applications. Many of the analyti...

OPENALEX cc-by DOI: https://doi.org/10.1088/1741-2552/ab0ab5

Deep learning approaches to biomedical image segmentation

Intisar Rizwan I Haque, Jeremiah J. Neubert • Informatics in Medicine Unlocked (2020)

The review covers automatic segmentation of images by means of deep learning approaches in the area of medical imaging. Current developments in machine learning, particularly related to deep learning, are proving instrumental in identificat...

OPENALEX cc-by-nc-nd DOI: https://doi.org/10.1016/j.imu.2020.100297

Image Segmentation Using Deep Learning: A Survey

Shervin Minaee, Yuri Y. Boykov, Fatih Porikli, Antonio Plaza et al. • IEEE Transactions on Pattern Analysis and Machine Intelligence (2021)

Image segmentation is a key task in computer vision and image processing with important applications such as scene understanding, medical image analysis, robotic perception, video surveillance, augmented reality, and image compression, amon...

OPENALEX Open Access DOI: https://doi.org/10.1109/tpami.2021.3059968

Deep Learning (CNN) and Transfer Learning: A Review

Jaya Gupta, Sunil Kumar Pathak, Gireesh Kumar • Journal of Physics Conference Series (2022)

Abstract Deep Learning is a machine learning area that has recently been used in a variety of industries. Unsupervised, semi-supervised, and supervised-learning are only a few of the strategies that have been developed to accommodate differ...

OPENALEX cc-by DOI: https://doi.org/10.1088/1742-6596/2273/1/012029

Systemic Review and Meta-Analysis: The Application of AI-Powered Drone Technology with Computer Vision and Deep Learning Networks in Waste Management

Tyrone Bright, Sarp Adali, Cristina Trois • Drones (2025)

As the generation of Municipal Solid Waste (MSW) has exponentially increased, this poses a challenge for waste managers, such as municipalities, to effectively control waste streams. If waste streams are not managed correctly, they negative...

DOAJ Open Access (DOAJ) DOI: 10.3390/drones9080550