Multimodal deep learning for biomedical data fusion: a review

SR Stahlschmidt, B Ulfenborg… - Briefings in …, 2022 - academic.oup.com
Biomedical data are becoming increasingly multimodal and thereby capture the underlying
complex relationships among biological processes. Deep learning (DL)-based data fusion …

Application of artificial intelligence technology in oncology: Towards the establishment of precision medicine

R Hamamoto, K Suvarna, M Yamada, K Kobayashi… - Cancers, 2020 - mdpi.com
Simple Summary Artificial intelligence (AI) technology has been advancing rapidly in recent
years and is being implemented in society. The medical field is no exception, and the clinical …

Multi-disease prediction based on deep learning: a survey

S **e, Z Yu, Z Lv - Computer Modeling in Engineering & …, 2021 - ingentaconnect.com
In recent years, the development of artificial intelligence (AI) and the gradual beginning of
AI's research in the medical field have allowed people to see the excellent prospects of the …

The development of a skin cancer classification system for pigmented skin lesions using deep learning

S **nai, N Yamazaki, Y Hirano, Y Sugawara, Y Ohe… - Biomolecules, 2020 - mdpi.com
Recent studies have demonstrated the usefulness of convolutional neural networks (CNNs)
to classify images of melanoma, with accuracies comparable to those achieved by …

Towards clinical application of artificial intelligence in ultrasound imaging

M Komatsu, A Sakai, A Dozen, K Shozu, S Yasutomi… - Biomedicines, 2021 - mdpi.com
Artificial intelligence (AI) is being increasingly adopted in medical research and applications.
Medical AI devices have continuously been approved by the Food and Drug Administration …

Comparison of vision transformers and convolutional neural networks in medical image analysis: a systematic review

S Takahashi, Y Sakaguchi, N Kouno… - Journal of Medical …, 2024 - Springer
In the rapidly evolving field of medical image analysis utilizing artificial intelligence (AI), the
selection of appropriate computational models is critical for accurate diagnosis and patient …

Machine learning for lung cancer diagnosis, treatment, and prognosis

Y Li, X Wu, P Yang, G Jiang… - Genomics, Proteomics and …, 2022 - academic.oup.com
The recent development of imaging and sequencing technologies enables systematic
advances in the clinical study of lung cancer. Meanwhile, the human mind is limited in …

The future is now? Clinical and translational aspects of “Omics” technologies

GL D'Adamo, JT Widdop… - Immunology and cell …, 2021 - Wiley Online Library
Big data has become a central part of medical research, as well as modern life
generally.“Omics” technologies include genomics, proteomics, microbiomics and …

Autoencoder-based multimodal prediction of non-small cell lung cancer survival

JG Ellen, E Jacob, N Nikolaou, N Markuzon - Scientific Reports, 2023 - nature.com
The ability to accurately predict non-small cell lung cancer (NSCLC) patient survival is
crucial for informing physician decision-making, and the increasing availability of multi …

Predicting deep learning based multi-omics parallel integration survival subtypes in lung cancer using reverse phase protein array data

S Takahashi, K Asada, K Takasawa, R Shimoyama… - Biomolecules, 2020 - mdpi.com
Mortality attributed to lung cancer accounts for a large fraction of cancer deaths worldwide.
With increasing mortality figures, the accurate prediction of prognosis has become essential …