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Image-based quantification of histological features as a function of spatial location using the Tissue Positioning SystemResearch in context

Ruichen Rong, Yonglong Wei, Lin Li, Tao Wang et al. • EBioMedicine (2023)

Summary: Background: Tissues such as the liver lobule, kidney nephron, and intestinal gland exhibit intricate patterns of zonated gene expression corresponding to distinct cell types and functions. To quantitatively understand zonation, it...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.ebiom.2023.104698

Deep Learning: A Review

Rocio Vargas, Amir Hosein Mosavi, Ramon Ruiz • Preprints.org (2018)

Deep learning is an emerging area of machine learning (ML) research. It comprises multiple hidden layers of artificial neural networks. The deep learn- ing methodology applies nonlinear transformations and model abstractions of high level i...

OPENALEX cc-by DOI: https://doi.org/10.20944/preprints201810.0218.v1

Deep Learning in Mobile and Wireless Networking: A Survey

Chaoyun Zhang, Paul Patras, Hamed Haddadi • IEEE Communications Surveys & Tutorials (2019)

The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traff...

OPENALEX Open Access DOI: https://doi.org/10.1109/comst.2019.2904897

Opportunities and obstacles for deep learning in biology and medicine

Travers H. Ching, Daniel Scott Himmelstein, Brett K. Beaulieu‐Jones, Alexandr A. Kalinin et al. • Journal of The Royal Society Interface (2018)

Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and m...

OPENALEX cc-by DOI: https://doi.org/10.1098/rsif.2017.0387 PMC: 5938574

Application of Deep Learning in Food: A Review

Lei Zhou, Chu Zhang, Fei Liu, Zhengjun Qiu et al. • Comprehensive Reviews in Food Science and Food Safety (2019)

Deep learning has been proved to be an advanced technology for big data analysis with a large number of successful cases in image processing, speech recognition, object detection, and so on. Recently, it has also been introduced in food sci...

OPENALEX DOI: https://doi.org/10.1111/1541-4337.12492

TransImg: A Translation Algorithm of Visible-to-Infrared Image Based on Generative Adversarial Network

Shuo Han, Bo Mo, Junwei Xu, Shizun Sun et al. • International Journal of Computational Intelligence Systems (2024)

Abstract Infrared images of sensitive targets are difficult to obtain and cannot meet the design and training needs of target detection and tracking algorithms for mobile platforms such as aircraft. This paper proposes an image translation ...

DOAJ Open Access (DOAJ) DOI: 10.1007/s44196-024-00674-7

Low-Cost Multi-Agent Navigation via Reinforcement Learning With Multi-Fidelity Simulator

Jiantao Qiu, Chao Yu, Weiling Liu, Tianxiang Yang et al. • IEEE Access (2021)

In recent years, reinforcement learning (RL) has been widely used to solve multi-agent navigation tasks, and a high-fidelity level for the simulator is critical to narrow the gap between simulation and real-world tasks. However, high-fideli...

DOAJ Open Access (DOAJ) DOI: 10.1109/ACCESS.2021.3085328

Rapid identification of bloodstream infection pathogens and drug resistance using Raman spectroscopy enhanced by convolutional neural networks

Haiquan Kang, Haiquan Kang, Ziling Wang, Jingfang Sun et al. • Frontiers in Microbiology (2024)

Bloodstream infections (BSIs) are a critical medical concern, characterized by elevated morbidity, mortality, extended hospital stays, substantial healthcare costs, and diagnostic challenges. The clinical outcomes for patients with BSI can ...

DOAJ Open Access (DOAJ) DOI: 10.3389/fmicb.2024.1428304

Deep Learning in Proteomics

Bo Wen, Wen‐Feng Zeng, Yuxing Liao, Zhiao Shi et al. • PROTEOMICS (2020)

Proteomics, the study of all the proteins in biological systems, is becoming a data-rich science. Protein sequences and structures are comprehensively catalogued in online databases. With recent advancements in tandem mass spectrometry (MS)...

OPENALEX cc-by DOI: https://doi.org/10.1002/pmic.201900335 PMC: 7757195

Text Data Augmentation for Deep Learning

Connor Shorten, Taghi M. Khoshgoftaar, Borko Furht • Journal Of Big Data (2021)

Natural Language Processing (NLP) is one of the most captivating applications of Deep Learning. In this survey, we consider how the Data Augmentation training strategy can aid in its development. We begin with the major motifs of Data Augme...

OPENALEX cc-by DOI: https://doi.org/10.1186/s40537-021-00492-0 PMC: 8287113

Deep learning, reinforcement learning, and world models

Yutaka Matsuo, Yann LeCun, Maneesh Sahani, Doina Precup et al. • Neural Networks (2022)

Deep learning (DL) and reinforcement learning (RL) methods seem to be a part of indispensable factors to achieve human-level or super-human AI systems. On the other hand, both DL and RL have strong connections with our brain functions and w...

OPENALEX cc-by-nc-nd DOI: https://doi.org/10.1016/j.neunet.2022.03.037

Deep Learning and Its Applications in Biomedicine

Chensi Cao, Feng Liu, Hai Tan, Deshou Song et al. • Genomics Proteomics & Bioinformatics (2018)

Advances in biological and medical technologies have been providing us explosive volumes of biological and physiological data, such as medical images, electroencephalography, genomic and protein sequences. Learning from these data facilitat...

OPENALEX cc-by DOI: https://doi.org/10.1016/j.gpb.2017.07.003 PMC: 6000200

Development of an automated two pronuclei detection system on time‐lapse embryo images using deep learning techniques

Noritaka Fukunaga, Sho Sanami, Hiroya Kitasaka, Yuji Tsuzuki et al. • Reproductive Medicine and Biology (2020)

Abstract Purpose To establish an automated pronuclei determination system by analysis using deep learning technology which is able to effectively learn with limited amount of supervised data. Methods An algorithm was developed by explicitly...

DOAJ Open Access (DOAJ) DOI: 10.1002/rmb2.12331

Comparing deep learning and pathologist quantification of cell-level PD-L1 expression in non-small cell lung cancer whole-slide images

Leander van Eekelen, Joey Spronck, Monika Looijen-Salamon, Shoko Vos et al. • Scientific Reports (2024)

Abstract Programmed death-ligand 1 (PD-L1) expression is currently used in the clinic to assess eligibility for immune-checkpoint inhibitors via the tumor proportion score (TPS), but its efficacy is limited by high interobserver variability...

DOAJ Open Access (DOAJ) DOI: 10.1038/s41598-024-57067-1

A novel network-level fused deep learning architecture with shallow neural network classifier for gastrointestinal cancer classification from wireless capsule endoscopy images

Muhammad Attique Khan, Usama Shafiq, Ameer Hamza, Anwar M. Mirza et al. • BMC Medical Informatics and Decision Making (2025)

Abstract Deep learning has significantly contributed to medical imaging and computer-aided diagnosis (CAD), providing accurate disease classification and diagnosis. However, challenges such as inter- and intra-class similarities, class imba...

DOAJ Open Access (DOAJ) DOI: 10.1186/s12911-025-02966-0

Review of Deep Learning for Language Modeling

WANG Sili, ZHANG Ling, YANG Heng, LIU Wei • Nongye tushu qingbao xuebao (2023)

[Purpose/Significance] Deep learning for language modeling is one of the major methods and advanced technologies to enhance language intelligence of machines at present, which has become an indispensable important technical means for automa...

DOAJ Open Access (DOAJ) DOI: 10.13998/j.cnki.issn1002-1248.23-0251