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Showing 20 of 195 academic articles

Deep Learning in Medical Imaging: General Overview

June‐Goo Lee, Sanghoon Jun, Young-Won Cho, Hyunna Lee et al. • Korean Journal of Radiology (2017)

The artificial neural network (ANN)-a machine learning technique inspired by the human neuronal synapse system-was introduced in the 1950s. However, the ANN was previously limited in its ability to solve actual problems, due to the vanishin...

OPENALEX cc-by-nc DOI: https://doi.org/10.3348/kjr.2017.18.4.570 PMC: 5447633

Deep Learning in Mining Biological Data

Mufti Mahmud, M. Shamim Kaiser, T. Martin McGinnity, Amir Hussain • Cognitive Computation (2021)

Recent technological advancements in data acquisition tools allowed life scientists to acquire multimodal data from different biological application domains. Categorized in three broad types (i.e. images, signals, and sequences), these data...

OPENALEX cc-by DOI: https://doi.org/10.1007/s12559-020-09773-x PMC: 7783296

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

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

Deep Learning in Medical Imaging

Mingyu Kim, Jihye Yun, Yongwon Cho, Keewon Shin et al. • Neurospine (2019)

The artificial neural network (ANN), one of the machine learning (ML) algorithms, inspired by the human brain system, was developed by connecting layers with artificial neurons. However, due to the low computing power and insufficient learn...

OPENALEX cc-by-nc DOI: https://doi.org/10.14245/ns.1938396.198 PMC: 6945006

An overview of deep learning in medical imaging focusing on MRI

Alexander Selvikvåg Lundervold, Arvid Lundervold • Zeitschrift für Medizinische Physik (2018)

What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years. The current boom started around 2009 when...

OPENALEX cc-by DOI: https://doi.org/10.1016/j.zemedi.2018.11.002

Deep metabolome: Applications of deep learning in metabolomics

Yotsawat Pomyen, Kwanjeera Wanichthanarak, Patcha Poungsombat, Johannes Francois Fahrmann et al. • Computational and Structural Biotechnology Journal (2020)

In the past few years, deep learning has been successfully applied to various omics data. However, the applications of deep learning in metabolomics are still relatively low compared to others omics. Currently, data pre-processing using con...

OPENALEX cc-by DOI: https://doi.org/10.1016/j.csbj.2020.09.033 PMC: 7575644

Deep learning as a tool for ecology and evolution

Marek L. Borowiec, Rebecca B. Dikow, Paul B. Frandsen, Alexander J. McKeeken et al. • Methods in Ecology and Evolution (2022)

Abstract Deep learning is driving recent advances behind many everyday technologies, including speech and image recognition, natural language processing and autonomous driving. It is also gaining popularity in biology, where it has been use...

OPENALEX cc-by-nc DOI: https://doi.org/10.1111/2041-210x.13901

Privacy-Preserving Deep Learning

Reza Shokri, Vitaly Shmatikov • ACM SIGSAC Conference on Computer and Communications Security (CCS) (2015)

Deep learning based on artificial neural networks is a very popular approach to modeling, classifying, and recognizing complex data such as images, speech, and text. The unprecedented accuracy of deep learning methods has turned them into t...

OPENALEX DOI: https://doi.org/10.1145/2810103.2813687

Deep Learning with Microfluidics for Biotechnology

Jason Riordon, Dušan Sovilj, Scott Sanner, David A. Sinton et al. • Trends in biotechnology (2018)

Advances in high-throughput and multiplexed microfluidics have rewarded biotechnology researchers with vast amounts of data but not necessarily the ability to analyze complex data effectively. Over the past few years, deep artificial neural...

OPENALEX cc-by DOI: https://doi.org/10.1016/j.tibtech.2018.08.005

DeepTox: Toxicity Prediction using Deep Learning

Andreas Mayr, Günter Klambauer, Thomas Unterthiner, Sepp Hochreiter • Frontiers in Environmental Science (2016)

The Tox21 Data Challenge has been the largest effort of the scientific community to compare computational methods for toxicity prediction. This challenge comprised 12,000 environmental chemicals and drugs which were measured for 12 differen...

OPENALEX cc-by DOI: https://doi.org/10.3389/fenvs.2015.00080

Deep Learning in Mammography

Anton S. Becker, Magda Marcon, Soleen Ghafoor, Moritz C. Wurnig et al. • Investigative Radiology (2017)

OBJECTIVES: The aim of this study was to evaluate the diagnostic accuracy of a multipurpose image analysis software based on deep learning with artificial neural networks for the detection of breast cancer in an independent, dual-center mam...

OPENALEX DOI: https://doi.org/10.1097/rli.0000000000000358

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 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

The rise of deep learning in drug discovery

Hongming Chen, Ola Engkvist, Yinhai Wang, Marcus Olivecrona et al. • Drug Discovery Today (2018)

Over the past decade, deep learning has achieved remarkable success in various artificial intelligence research areas. Evolved from the previous research on artificial neural networks, this technology has shown superior performance to other...

OPENALEX cc-by DOI: https://doi.org/10.1016/j.drudis.2018.01.039

Deep Learning in Drug Discovery

Erik Gawehn, Jan A. Hiss, Gisbert Schneider • Molecular Informatics (2015)

Artificial neural networks had their first heyday in molecular informatics and drug discovery approximately two decades ago. Currently, we are witnessing renewed interest in adapting advanced neural network architectures for pharmaceutical ...

OPENALEX DOI: https://doi.org/10.1002/minf.201501008

Deep Learning: A Primer for Radiologists

Gabriel Chartrand, Phillip M. Cheng, Eugene Vorontsov, Michal Drozdzal et al. • Radiographics (2017)

Deep learning is a class of machine learning methods that are gaining success and attracting interest in many domains, including computer vision, speech recognition, natural language processing, and playing games. Deep learning methods prod...

OPENALEX DOI: https://doi.org/10.1148/rg.2017170077

Deep learning for computational chemistry

Garrett B. Goh, Nathan O. Hodas, Abhinav Vishnu • Journal of Computational Chemistry (2017)

The rise and fall of artificial neural networks is well documented in the scientific literature of both computer science and computational chemistry. Yet almost two decades later, we are now seeing a resurgence of interest in deep learning,...

OPENALEX Open Access DOI: https://doi.org/10.1002/jcc.24764

Deep Learning for Health Informatics

Daniele Ravì, Charence Wong, Fani Deligianni, Melissa Berthelot et al. • IEEE Journal of Biomedical and Health Informatics (2016)

With a massive influx of multimodality data, the role of data analytics in health informatics has grown rapidly in the last decade. This has also prompted increasing interests in the generation of analytical, data driven models based on mac...

OPENALEX cc-by DOI: https://doi.org/10.1109/jbhi.2016.2636665