AIMC Topic: Brain Neoplasms

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Y-net: a reducing gaussian noise convolutional neural network for MRI brain tumor classification with NADE concatenation.

Biomedical physics & engineering express
Brain tumors are among the most serious cancers that can have a negative impact on a person's quality of life. The magnetic resonance imaging (MRI) analysis detects abnormal cell growth in the skull. Recently, machine learning models such as artifici...

Aggregation-and-Attention Network for brain tumor segmentation.

BMC medical imaging
BACKGROUND: Glioma is a malignant brain tumor; its location is complex and is difficult to remove surgically. To diagnosis the brain tumor, doctors can precisely diagnose and localize the disease using medical images. However, the computer-assisted d...

Optical tissue clearing and machine learning can precisely characterize extravasation and blood vessel architecture in brain tumors.

Communications biology
Precise methods for quantifying drug accumulation in brain tissue are currently very limited, challenging the development of new therapeutics for brain disorders. Transcardial perfusion is instrumental for removing the intravascular fraction of an in...

Robot assisted laser-interstitial thermal therapy with iSYS1 and Visualase: how I do it.

Acta neurochirurgica
BACKGROUND: Laser-interstitial thermal therapy (LITT) is an ablative treatment based on a surgically implanted laser-emitting catheter to induce a focal ablation of the pathological tissue. The main indications in neurosurgery are primary brain tumor...

Performance optimisation of deep learning models using majority voting algorithm for brain tumour classification.

Computers in biology and medicine
BACKGROUND: Although biopsy is the gold standard for tumour grading, being invasive, this procedure also proves fatal to the brain. Thus, non-invasive methods for brain tumour grading are urgently needed. Here, a magnetic resonance imaging (MRI)-base...

Multispectral co-occurrence of wavelet coefficients for malignancy assessment of brain tumors.

PloS one
Brain tumor is not most common, but truculent type of cancer. Therefore, correct prediction of its aggressiveness nature at an early stage would influence the treatment strategy. Although several diagnostic methods based on different modalities exist...

Predicting cell behaviour parameters from glioblastoma on a chip images. A deep learning approach.

Computers in biology and medicine
The broad possibilities offered by microfluidic devices in relation to massive data monitoring and acquisition open the door to the use of deep learning technologies in a very promising field: cell culture monitoring. In this work, we develop a metho...

Automatic brain tumour diagnostic method based on a back propagation neural network and an extended set-membership filter.

Computer methods and programs in biomedicine
BACKGROUND: Diagnosing brain tumours remains a challenging task in clinical practice. Despite their questionable accuracy, magnetic resonance image (MRI) scans are presently considered the optimal facility for assessing the growth of tumours. However...

Resection of Intracranial Tumors with a Robotic-Assisted Digital Microscope: A Preliminary Experience with Robotic Scope.

World neurosurgery
BACKGROUND: Magnified intraoperative visualization is of paramount importance during microsurgical procedures. Although the introduction of the operating microscope represented one of the most relevant innovations in modern neurosurgery, surgical vis...

Fully Automated MR Detection and Segmentation of Brain Metastases in Non-small Cell Lung Cancer Using Deep Learning.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Non-small cell lung cancer (NSCLC) is the most common tumor entity spreading to the brain and up to 50% of patients develop brain metastases (BMs). Detection of BMs on MRI is challenging with an inherent risk of missed diagnosis.