AIMC Topic: Neoplasms

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Deep Collocative Learning for Immunofixation Electrophoresis Image Analysis.

IEEE transactions on medical imaging
Immunofixation Electrophoresis (IFE) analysis is of great importance to the diagnosis of Multiple Myeloma, which is among the top-9 cancer killers in the United States, but has rarely been studied in the context of deep learning. Two possible reasons...

The structure-based cancer-related single amino acid variation prediction.

Scientific reports
Single amino acid variation (SAV) is an amino acid substitution of the protein sequence that can potentially influence the entire protein structure or function, as well as its binding affinity. Protein destabilization is related to diseases, includin...

Long-term cancer survival prediction using multimodal deep learning.

Scientific reports
The age of precision medicine demands powerful computational techniques to handle high-dimensional patient data. We present MultiSurv, a multimodal deep learning method for long-term pan-cancer survival prediction. MultiSurv uses dedicated submodels ...

Artificial Intelligence in Cancer Care: Legal and Regulatory Dimensions.

The oncologist
Considering that artificial intelligence (AI) technologies have the potential to change cancer care, this article discusses the AI features of which oncologist should most be aware.

Artificial intelligence in tumor subregion analysis based on medical imaging: A review.

Journal of applied clinical medical physics
Medical imaging is widely used in the diagnosis and treatment of cancer, and artificial intelligence (AI) has achieved tremendous success in medical image analysis. This paper reviews AI-based tumor subregion analysis in medical imaging. We summarize...

[Artificial Intelligence in epidemiology].

Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique
Artificial Intelligence can be leveraged to analyze great amounts of data. It can be used on images or textual data to define the epidemiology of diseases, such as cancer. In this review, we will present and discuss the applications of AI in this set...

Cancer diagnosis using generative adversarial networks based on deep learning from imbalanced data.

Computers in biology and medicine
BACKGROUND AND OBJECTIVE: Cancer is a serious global disease due to its high mortality, and the key to effective treatment is accurate diagnosis. However, limited by sampling difficulty and actual sample size in clinical practice, data imbalance is a...

Evaluating machine learning methodologies for identification of cancer driver genes.

Scientific reports
Cancer is driven by distinctive sorts of changes and basic variations in genes. Recognizing cancer driver genes is basic for accurate oncological analysis. Numerous methodologies to distinguish and identify drivers presently exist, but efficient tool...

Impact of deep learning-determined smoking status on mortality of cancer patients: never too late to quit.

ESMO open
BACKGROUND: Persistent smoking after cancer diagnosis is associated with increased overall mortality (OM) and cancer mortality (CM). According to the 2020 Surgeon General's report, smoking cessation may reduce CM but supporting evidence is not wide. ...