AI Medical Compendium Topic:
Neoplasms

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The use of artificial intelligence tools in cancer detection compared to the traditional diagnostic imaging methods: An overview of the systematic reviews.

PloS one
BACKGROUND AND PURPOSE: In comparison to conventional medical imaging diagnostic modalities, the aim of this overview article is to analyze the accuracy of the application of Artificial Intelligence (AI) techniques in the identification and diagnosis...

Identifying cancer risks using spectral subset feature selection based on multi-layer perception neural network for premature treatment.

Computer methods in biomechanics and biomedical engineering
Recently, human beings have been affected mainly by dreadful cancer diseases. Predicting cancer risk levels is a major challenge in biomedical research for feature selection and classification at the margins. To resolve this problem, we propose a Sub...

XA4C: eXplainable representation learning via Autoencoders revealing Critical genes.

PLoS computational biology
Machine Learning models have been frequently used in transcriptome analyses. Particularly, Representation Learning (RL), e.g., autoencoders, are effective in learning critical representations in noisy data. However, learned representations, e.g., the...

Multi-target meridians classification based on the topological structure of anti-cancer phytochemicals using deep learning.

Journal of ethnopharmacology
ETHNOPHARMACOLOGICAL RELEVANCE: Traditional Chinese medicine (TCM) meridian is the key theoretical guidance of prescription against tumor in clinical practice. However, there is no scientific and systematic verification of therapeutic action of herbs...

Imaging Analytics using Artificial Intelligence in Oncology: A Comprehensive Review.

Clinical oncology (Royal College of Radiologists (Great Britain))
The present era has seen a surge in artificial intelligence-related research in oncology, mainly using deep learning, because of powerful computer hardware, improved algorithms and the availability of large amounts of data from open-source domains an...

Classification of cancer cells at the sub-cellular level by phonon microscopy using deep learning.

Scientific reports
There is a consensus about the strong correlation between the elasticity of cells and tissue and their normal, dysplastic, and cancerous states. However, developments in cell mechanics have not seen significant progress in clinical applications. In t...

Imaging biomarkers and radiomics in pediatric oncology: a view from the PRIMAGE (PRedictive In silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, Empowered by imaging biomarkers) project.

Pediatric radiology
This review paper presents the practical development of imaging biomarkers in the scope of the PRIMAGE (PRedictive In silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, Empowered by imaging biomarkers) project, as a n...

Deep learning for tumor margin identification in electromagnetic imaging.

Scientific reports
In this work, a novel method for tumor margin identification in electromagnetic imaging is proposed to optimize the tumor removal surgery. This capability will enable the visualization of the border of the cancerous tissue for the surgeon prior or du...

Deep learning in MRI-guided radiation therapy: A systematic review.

Journal of applied clinical medical physics
Recent advances in MRI-guided radiation therapy (MRgRT) and deep learning techniques encourage fully adaptive radiation therapy (ART), real-time MRI monitoring, and the MRI-only treatment planning workflow. Given the rapid growth and emergence of new...

Training of epitope-TCR prediction models with healthy donor-derived cancer-specific T cells.

Methods in cell biology
Discovery of epitope-specific T-cell receptors (TCRs) for cancer therapies is a time consuming and expensive procedure that usually requires a large amount of patient cells. To maximize information from and minimize the need of precious samples in ca...