AIMC Topic: Brain Neoplasms

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Detection of brain tumors using a transfer learning-based optimized ResNet152 model in MR images.

Computers in biology and medicine
Brain tumors are incredibly harmful and can drastically reduce life expectancy. Most researchers use magnetic resonance (MR) scans to detect tumors because they can provide detailed images of the affected area. Recently, AI-based deep learning method...

EAMAPG: Explainable Adversarial Model Analysis via Projected Gradient Descent.

Computers in biology and medicine
Despite the outstanding performance of deep learning (DL) models, their interpretability remains a challenging topic. In this study, we address the transparency of DL models in medical image analysis by introducing a novel interpretability method usi...

Real-time adaptive proton therapy: An AI-based spatio-temporal mono-energetic dose calculation model (CC-LSTM).

Computers in biology and medicine
PURPOSE: To develop a fully AI-based dose estimation model capable of learning and estimating single pencil beam dose distributions, and to verify its performance by testing the model's generalizability on unseen, previously delivered treatment plans...

Cross prior Bayesian attention with correlated inception and residual learning for brain tumor classification using MR images (CB-CIRL Net).

Journal of neuroscience methods
BACKGROUND: Brain tumor classification from magnetic resonance (MR) images is crucial for early diagnosis and effective treatment planning. However, the homogeneity of tumors across different categories poses a challenge. Although, attention-based co...

A privacy-preserved horizontal federated learning for malignant glioma tumour detection using distributed data-silos.

PloS one
Malignant glioma is the uncontrollable growth of cells in the spinal cord and brain that look similar to the normal glial cells. The most essential part of the nervous system is glial cells, which support the brain's functioning prominently. However,...

Deformation registration based on reconstruction of brain MRI images with pathologies.

Medical & biological engineering & computing
Deformable registration between brain tumor images and brain atlas has been an important tool to facilitate pathological analysis. However, registration of images with tumors is challenging due to absent correspondences induced by the tumor. Furtherm...

Enhancing deep learning methods for brain metastasis detection through cross-technique annotations on SPACE MRI.

European radiology experimental
BACKGROUND: Gadolinium-enhanced "sampling perfection with application-optimized contrasts using different flip angle evolution" (SPACE) sequence allows better visualization of brain metastases (BMs) compared to "magnetization-prepared rapid acquisiti...

A deep ensemble learning framework for glioma segmentation and grading prediction.

Scientific reports
The segmentation and risk grade prediction of gliomas based on preoperative multimodal magnetic resonance imaging (MRI) are crucial tasks in computer-aided diagnosis. Due to the significant heterogeneity between and within tumors, existing methods ma...

Personalized auto-segmentation for magnetic resonance imaging-guided adaptive radiotherapy of large brain metastases.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: Magnetic resonance-guided adaptive radiotherapy (MRgART) may improve the efficacy of large brain metastases (BMs)(≥2 cm), whereas the workflow requires optimized. This study develops a two-stage, personalized deep learning aut...