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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

VulDeePecker: A Deep Learning-Based System for Vulnerability Detection

Zhen Li, Deqing Zou, Shouhuai Xu, Xinyu Ou et al. • Network and Distributed System Security Symposium (2018)

The automatic detection of software vulnerabilities is an important research problem.However, existing solutions to this problem rely on human experts to define features and often miss many vulnerabilities (i.e., incurring high false negati...

OPENALEX Open Access DOI: https://doi.org/10.14722/ndss.2018.23158

Multimodal Deep Learning

Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam et al. (2011)

Deep networks have been successfully applied to unsupervised feature learning for single modalities (e.g., text, images or audio). In this work, we propose a novel application of deep networks to learn features over multiple modalities. We ...

OPENALEX

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

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

Deep learning in cancer diagnosis, prognosis and treatment selection

Khoa Anh Tran, Olga Kondrashova, Andrew Peter Bradley, Elizabeth D. Williams et al. • Genome Medicine (2021)

Deep learning is a subdiscipline of artificial intelligence that uses a machine learning technique called artificial neural networks to extract patterns and make predictions from large data sets. The increasing adoption of deep learning acr...

OPENALEX cc-by DOI: https://doi.org/10.1186/s13073-021-00968-x PMC: 8477474

A Comprehensive Survey on Community Detection With Deep Learning

Xing Su, Shan Xue, Fanzhen Liu, Jia Wu et al. • IEEE Transactions on Neural Networks and Learning Systems (2022)

Detecting a community in a network is a matter of discerning the distinct features and connections of a group of members that are different from those in other communities. The ability to do this is of great significance in network analysis...

OPENALEX Open Access DOI: https://doi.org/10.1109/tnnls.2021.3137396

Deep learning in food category recognition

Yudong Zhang, Lijia Deng, Hengde Zhu, Wei Wang et al. • Information Fusion (2023)

Integrating artificial intelligence with food category recognition has been a field of interest for research for the past few decades. It is potentially one of the next steps in revolutionizing human interaction with food. The modern advent...

OPENALEX cc-by DOI: https://doi.org/10.1016/j.inffus.2023.101859

ACtriplet: An improved deep learning model for activity cliffs prediction by integrating triplet loss and pre-training

Xinxin Yu, Yimeng Wang, Long Chen, Weihua Li et al. • Journal of Pharmaceutical Analysis (2025)

Activity cliffs (ACs) are generally defined as pairs of similar compounds that only differ by a minor structural modification but exhibit a large difference in their binding affinity for a given target. ACs offer crucial insights that aid m...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.jpha.2025.101317

Deep learning for power quality

Roger Alves de Oliveira, Math Bollen • Electric Power Systems Research (2022)

This paper aims to introduce deep learning to the power quality community by reviewing the latest applications and discussing the open challenges of this technology. Publications covering deep learning to power quality are stratified in ter...

OPENALEX cc-by DOI: https://doi.org/10.1016/j.epsr.2022.108887

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

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