AIMC Topic: Neoplasms

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Reinforcement learning-based control of tumor growth under anti-angiogenic therapy.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: In recent decades, cancer has become one of the most fatal and destructive diseases which is threatening humans life. Accordingly, different types of cancer treatment are studied with the main aim to have the best treatment...

Machine learning for automated quality assurance in radiotherapy: A proof of principle using EPID data description.

Medical physics
PURPOSE: Developing automated methods to identify task-driven quality assurance (QA) procedures is key toward increasing safety, efficacy, and efficiency. We investigate the use of machine learning (ML) methods for possible visualization, automation,...

Deep convolutional neural networks for accurate somatic mutation detection.

Nature communications
Accurate detection of somatic mutations is still a challenge in cancer analysis. Here we present NeuSomatic, the first convolutional neural network approach for somatic mutation detection, which significantly outperforms previous methods on different...

Rise of the Machines: Advances in Deep Learning for Cancer Diagnosis.

Trends in cancer
Deep learning refers to a set of computer models that have recently been used to make unprecedented progress in the way computers extract information from images. These algorithms have been applied to tasks in numerous medical specialties, most exten...

Machine learning in whole-body MRI: experiences and challenges from an applied study using multicentre data.

Clinical radiology
Machine learning is now being increasingly employed in radiology to assist with tasks such as automatic lesion detection, segmentation, and characterisation. We are currently involved in an National Institute of Health Research (NIHR)-funded project,...

Supervised Discriminative Sparse PCA for Com-Characteristic Gene Selection and Tumor Classification on Multiview Biological Data.

IEEE transactions on neural networks and learning systems
Principal component analysis (PCA) has been used to study the pathogenesis of diseases. To enhance the interpretability of classical PCA, various improved PCA methods have been proposed to date. Among these, a typical method is the so-called sparse P...

Active learning using rough fuzzy classifier for cancer prediction from microarray gene expression data.

Journal of biomedical informatics
Cancer classification from microarray gene expression data is one of the important areas of research in the field of computational biology and bioinformatics. Traditional supervised techniques often fail to produce desired accuracy as the number of c...

The Adaptive Hermite Fractal Tree (AHFT): a novel surgical 3D path planning approach with curvature and heading constraints.

International journal of computer assisted radiology and surgery
PURPOSE: In the context of minimally invasive neurosurgery, steerable needles such as the one developed within the Horizon2020-funded EDEN2020 project (Frasson et al. in Proc Inst Mech Eng Part H J Eng Med 224(6):775-88, 2010. https://doi.org/10.1243...

Artificial intelligence in cancer imaging: Clinical challenges and applications.

CA: a cancer journal for clinicians
Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered data with nuanced decision making. Cancer offers a unique context for medical decisions given not only its variegated forms with evolution of disease but al...

Novel Regularization Method for Biomarker Selection and Cancer Classification.

IEEE/ACM transactions on computational biology and bioinformatics
Variable selection has attracted more attention in big data and machine learning fields. In high dimensional data analysis, many relevant variables or variable groups are widely found. For example, people pay more interests to biological pathway or r...