AIMC Topic: Brain Neoplasms

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Enhanced 3D dose prediction for hypofractionated SRS (gamma knife radiosurgery) in brain tumor using cascaded-deep-supervised convolutional neural network.

Physical and engineering sciences in medicine
Gamma Knife radiosurgery (GKRS) is a well-established technique in radiation therapy (RT) for treating small-size brain tumors. It administers highly concentrated doses during each treatment fraction, with even minor dose errors posing a significant ...

Mask region-based convolutional neural network and VGG-16 inspired brain tumor segmentation.

Scientific reports
The process of brain tumour segmentation entails locating the tumour precisely in images. Magnetic Resonance Imaging (MRI) is typically used by doctors to find any brain tumours or tissue abnormalities. With the use of region-based Convolutional Neur...

Deep Learning-Based Techniques in Glioma Brain Tumor Segmentation Using Multi-Parametric MRI: A Review on Clinical Applications and Future Outlooks.

Journal of magnetic resonance imaging : JMRI
This comprehensive review explores the role of deep learning (DL) in glioma segmentation using multiparametric magnetic resonance imaging (MRI) data. The study surveys advanced techniques such as multiparametric MRI for capturing the complex nature o...

AG-MSTLN-EL: A Multi-source Transfer Learning Approach to Brain Tumor Detection.

Journal of imaging informatics in medicine
The analysis of medical images (MI) is an important part of advanced medicine as it helps detect and diagnose various diseases early. Classifying brain tumors through magnetic resonance imaging (MRI) poses a challenge demanding accurate models for ef...

A data-centric machine learning approach to improve prediction of glioma grades using low-imbalance TCGA data.

Scientific reports
Accurate prediction and grading of gliomas play a crucial role in evaluating brain tumor progression, assessing overall prognosis, and treatment planning. In addition to neuroimaging techniques, identifying molecular biomarkers that can guide the dia...

Deep learning-based IDH1 gene mutation prediction using histopathological imaging and clinical data.

Computers in biology and medicine
In the field of histopathology, many studies on the classification of whole slide images (WSIs) using artificial intelligence (AI) technology have been reported. We have studied the disease progression assessment of glioma. Adult-type diffuse gliomas...

An XAI-enhanced efficientNetB0 framework for precision brain tumor detection in MRI imaging.

Journal of neuroscience methods
BACKGROUND: Accurately diagnosing brain tumors from MRI scans is crucial for effective treatment planning. While traditional methods heavily rely on radiologist expertise, the integration of AI, particularly Convolutional Neural Networks (CNNs), has ...

Neurological insights into brain-targeted cancer therapy and bioinspired microrobots.

Drug discovery today
Cancer, a multifaceted and pernicious disease, continuously challenges medicine, requiring innovative treatments. Brain cancers pose unique and daunting challenges due to the intricacies of the central nervous system and the blood-brain barrier. In t...

AI-assisted Segmentation Tool for Brain Tumor MR Image Analysis.

Journal of imaging informatics in medicine
TumorPrism3D software was developed to segment brain tumors with a straightforward and user-friendly graphical interface applied to two- and three-dimensional brain magnetic resonance (MR) images. The MR images of 185 patients (103 males, 82 females)...

CFINet: Cross-Modality MRI Feature Interaction Network for Pseudoprogression Prediction of Glioblastoma.

Journal of computational biology : a journal of computational molecular cell biology
Pseudoprogression (PSP) is a related reaction of glioblastoma treatment, and misdiagnosis can lead to unnecessary intervention. Magnetic resonance imaging (MRI) provides cross-modality images for PSP prediction studies. However, how to effectively us...