AIMC Topic: Neoplasms

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Application of Artificial Intelligence Nuclear Medicine Automated Images Based on Deep Learning in Tumor Diagnosis.

Journal of healthcare engineering
In order to correctly obtain normal tissues and organs and tumor lesions, the research on multimodal medical image segmentation based on deep learning fully automatic segmentation algorithm is more meaningful. This article aims to study the applicati...

Network biology and artificial intelligence drive the understanding of the multidrug resistance phenotype in cancer.

Drug resistance updates : reviews and commentaries in antimicrobial and anticancer chemotherapy
Globally with over 10 million deaths per year, cancer is the most transversal disease across countries, cultures, and ethnicities, affecting both developed and developing regions. Tumorigenesis is dynamically altered by distinct events and can be let...

Development of a deep learning-based patient-specific target contour prediction model for markerless tumor positioning.

Medical physics
PURPOSE: For pancreatic cancer patients, image guided radiation therapy and real-time tumor tracking (RTTT) techniques can deliver radiation to the target accurately. Currently, for the radiation therapy machine with kV X-ray imaging systems, interna...

Insights into cell classification based on combination of multiple cellular mechanical phenotypes by using machine learning algorithm.

Journal of the mechanical behavior of biomedical materials
Although cellular elastic property (CEP, also known as cellular elastic modulus) has been frequently reported as a biomarker to distinguish some cancerous cells from their benign counterparts, it cannot be adopted as a universal hallmark to be applie...

Integrated Pipeline of Rapid Isolation and Analysis of Human Plasma Exosomes for Cancer Discrimination Based on Deep Learning of MALDI-TOF MS Fingerprints.

Analytical chemistry
Plasma exosomes have shown great potential for liquid biopsy in clinical cancer diagnosis. Herein, we present an integrated strategy for isolating and analyzing exosomes from human plasma rapidly and then discriminating different cancers excellently ...

Mini-batch optimization enables training of ODE models on large-scale datasets.

Nature communications
Quantitative dynamic models are widely used to study cellular signal processing. A critical step in modelling is the estimation of unknown model parameters from experimental data. As model sizes and datasets are steadily growing, established paramete...

Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer sample.

Genome biology
BACKGROUND: Accurate detection of somatic mutations is challenging but critical in understanding cancer formation, progression, and treatment. We recently proposed NeuSomatic, the first deep convolutional neural network-based somatic mutation detecti...

Deep-Learning-Based Cancer Profiles Classification Using Gene Expression Data Profile.

Journal of healthcare engineering
The quantity of data required to give a valid analysis grows exponentially as machine learning dimensionality increases. In a single experiment, microarrays or gene expression profiling assesses and determines gene expression levels and patterns in v...