AIMC Topic: Brain Neoplasms

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An Intelligent Diagnosis Method of Brain MRI Tumor Segmentation Using Deep Convolutional Neural Network and SVM Algorithm.

Computational and mathematical methods in medicine
Among the currently proposed brain segmentation methods, brain tumor segmentation methods based on traditional image processing and machine learning are not ideal enough. Therefore, deep learning-based brain segmentation methods are widely used. In t...

Deep learning for glioblastoma segmentation using preoperative magnetic resonance imaging identifies volumetric features associated with survival.

Acta neurochirurgica
BACKGROUND: Measurement of volumetric features is challenging in glioblastoma. We investigate whether volumetric features derived from preoperative MRI using a convolutional neural network-assisted segmentation is correlated with survival.

Radiomics in radiation oncology-basics, methods, and limitations.

Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]
Over the past years, the quantity and complexity of imaging data available for the clinical management of patients with solid tumors has increased substantially. Without the support of methods from the field of artificial intelligence (AI) and machin...

DDeep3M: Docker-powered deep learning for biomedical image segmentation.

Journal of neuroscience methods
BACKGROUND: Deep learning models are turning out to be increasingly popular in biomedical image processing. The fruitful utilization of these models, in most cases, is substantially restricted by the complicated configuration of computational environ...

Clinical-Grade Detection of Microsatellite Instability in Colorectal Tumors by Deep Learning.

Gastroenterology
BACKGROUND & AIMS: Microsatellite instability (MSI) and mismatch-repair deficiency (dMMR) in colorectal tumors are used to select treatment for patients. Deep learning can detect MSI and dMMR in tumor samples on routine histology slides faster and le...

Radiomics in neuro-oncology: Basics, workflow, and applications.

Methods (San Diego, Calif.)
Over the last years, the amount, variety, and complexity of neuroimaging data acquired in patients with brain tumors for routine clinical purposes and the resulting number of imaging parameters have substantially increased. Consequently, a timely and...

Implementation of model explainability for a basic brain tumor detection using convolutional neural networks on MRI slices.

Neuroradiology
PURPOSE: While neural networks gain popularity in medical research, attempts to make the decisions of a model explainable are often only made towards the end of the development process once a high predictive accuracy has been achieved.

Multiclass magnetic resonance imaging brain tumor classification using artificial intelligence paradigm.

Computers in biology and medicine
MOTIVATION: Brain or central nervous system cancer is the tenth leading cause of death in men and women. Even though brain tumour is not considered as the primary cause of mortality worldwide, 40% of other types of cancer (such as lung or breast canc...

Robot technology identifies a Parkinsonian therapeutics repurpose to target stem cells of glioblastoma.

CNS oncology
Glioblastoma is a heterogeneous lethal disease, regulated by a stem-cell hierarchy and the neurotransmitter microenvironment. The identification of chemotherapies targeting individual cancer stem cells is a clinical need. A robotic workstation was ...