Brain Cancer

A Tumor Aware DenseNet Swin Hybrid Learning with Boosted and Hierarchical Feature Spaces for Large-Scale Brain MRI Classification

This study proposes an efficient Densely Swin Hybrid (EDSH) framework for brain tumor MRI analysis, designed to jointly capture fine grained texture patterns and long range contextual dependencies. Two tumor aware experimental setups are introduced to address class-specific diagnostic challenges. The first setup employs a Boosted Feature Space (BFS), where independently customized DenseNet and Swi...

Ensemble Machine Learning Approaches Predict Survival in Lower-Grade Glioma Based on Glycosphingolipid Gene Expression and Metabolic Modelling

Glycosphingolipids (GSLs) are essential components of biological membranes with important roles in cell signalling. Disrupted GSL metabolism is associated with malignancy across a range of cancers, with different GSLs implicated in distinct tumours. GSLs have potential mechanistic roles in cancer; however, their functions in Lower Grade Gliomas (LGGs) remain poorly understood. We present ensemble ...

Fusion-Restoration Image Processing Algorithm to Improve the High-Temperature Deformation Measurement

In the deformation measurement of high-temperature structures, image degradation caused by thermal radiation and random errors introduced by heat haze...

Explainable histomorphology-based survival prediction of glioblastoma, IDH-wildtype

Glioblastoma, IDH-wildtype (GBM-IDHwt) is the most common malignant brain tumor. Histomorphology is a crucial component of the integrated diagnosis of...

Karhunen-Loève Expansion-Based Residual Anomaly Map for Resource-Efficient Glioma MRI Segmentation

Accurate segmentation of brain tumors is essential for clinical diagnosis and treatment planning. Deep learning is currently the state-of-the-art for ...

GARD: Genomic Data based Drug Repurposing in Head and Neck Cancer with Large Language Model Validation

Background/ObjectivesHead and neck cancer (HNC) represents the seventh most common cancer diagnosis globally, yet current treatments, including surger...

Advancing Brain Tumor Diagnosis Using Deep Learning: A Systematic Review on Glioma Segmentation and Classification on Multiparametric MRI

Brain tumors are among the most lethal cancers with gliomas representing the most morphologically complex type. Precise and time efficient glioma segm...

Onco-Seg: Adapting Promptable Concept Segmentation for Multi-Modal Medical Imaging

Medical image segmentation remains a critical bottleneck in clinical workflows, from diagnostic radiology to radiation oncology treatment planning. We...

Real-Time Dose-Guided Radiation Therapy.

Dramatic strides have been made in real-time adaptive radiation therapy, where treating single tumors as dynamic but rigid bodies has demonstrated a h...

F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement

F3-Net is a foundation model designed to overcome persistent challenges in clinical medical image segmentation, including reliance on complete multi...

Optimization-based image reconstruction regularized with inter-spectral structural similarity for limited-angle dual-energy cone-beam CT.

. Limited-angle dual-energy (DE) cone-beam CT (CBCT) is considered as a potential solution to achieve fast and low-dose DE imaging on current CBCT sca...

Deep Survival Analysis in Multimodal Medical Data: A Parametric and Probabilistic Approach with Competing Risks

Accurate survival prediction is critical in oncology for prognosis and treatment planning. Traditional approaches often rely on a single data modali...

Contrast-enhanced image synthesis using latent diffusion model for precise online tumor delineation in MRI-guided adaptive radiotherapy for brain metastases.

Magnetic resonance imaging-guided adaptive radiotherapy (MRIgART) is a promising technique for long-course radiotherapy of large-volume brain metastas...

Radiation and contrast dose reduction in coronary CT angiography for slender patients with 70 kV tube voltage and deep learning image reconstruction.

OBJECTIVE: To evaluate the radiation and contrast dose reduction potential of combining 70 kV with deep learning image reconstruction (DLIR) in corona...

Acquisition and Reconstruction Techniques for Coronary CT Angiography: Current Status and Trends over the Past Decade.

Coronary CT angiography (CCTA) has been widely used as a noninvasive modality for accurate assessment of coronary artery disease (CAD) in clinical set...

Agreement between Routine-Dose and Lower-Dose CT with and without Deep Learning-based Denoising for Active Surveillance of Solid Small Renal Masses: A Multiobserver Study.

Purpose To assess the agreement between routine-dose (RD) and lower-dose (LD) contrast-enhanced CT scans, with and without Digital Imaging and Communi...

A combined attention mechanism for brain tumor segmentation of lower-grade glioma in magnetic resonance images.

Low-grade gliomas (LGGs) are among the most problematic brain tumors to reliably segment in FLAIR MRI, and effective delineation of these lesions is c...

Deep Learning Image Reconstruction (DLIR) Algorithm to Maintain High Image Quality and Diagnostic Accuracy in Quadruple-low CT Angiography of Children with Pulmonary Sequestration: A Case Control Study.

RATIONALE AND OBJECTIVES: CT angiography (CTA) is a commonly used clinical examination to detect abnormal arteries and diagnose pulmonary sequestratio...

Transforming the management of radiotherapy-induced hypothyroidism in nasopharyngeal carcinoma through an Innovative individualized radiation dosage model: A multicenter retrospective analysis.

PURPOSE: Current guidelines for thyroid radiation dose prescription lack uniformity and fail to consider the unique characteristics of individual pati...

REPAIR: Reciprocal assistance imputation-representation learning for glioma diagnosis with incomplete MRI sequences.

The absence of MRI sequences is a common occurrence in clinical practice, posing a significant challenge for prediction modeling of non-invasive diagn...