Brain Cancer

Anatomically-conditioned Latent Diffusion Model for Data-Efficient Few-Shot Cross-Domain 3D Glioma MRI Synthesis

Accurate classification of diffuse gliomas is often hindered by domain shifts across centers and a lack of large, annotated datasets. We propose the Anatomically-conditioned Latent Diffusion Model (ALDM), a novel framework for data-efficient, few-shot 3D volumetric MRI synthesis. ALDM utilizes a two-stage approach: a 3D variational autoencoder learns anatomical priors from a data-rich source domai...

BrainG3N: A Dual-Purpose Tokenizer for Controllable 3D Brain MRI Generation

Three-dimensional (3D) brain MRI is central to clinical neurology and neuro-oncology, where generative models could augment under-represented cohorts, simulate disease trajectories, and support privacy-preserving data sharing. Latent diffusion has been the go-to solution for modeling imaging data, but it places two competing demands on the tokenizer: encoder embeddings must retain the clinical inf...

Toward Training-Free Zero-Shot Anomaly Detection in 3D Medical Images: A Batch-Based Approach Using 2D Foundation Models

Zero-shot anomaly detection (ZSAD) is attractive for medical imaging because clinical systems must handle heterogeneous acquisition protocols, changin...

Confidence is Not Reliability: Rethinking MC Dropout in Brain Tumour Segmentation

Glioma segmentation in multiparametric MRI is a critical component of treatment planning. A segmentation model that fails silently on treatment-critic...

Natural Language Processing Based Solution for Labeling Brain Metastasis Identified in Radiology Reports

Abstract Purpose: Brain metastases (BM) far exceed primary CNS tumours and constitute the majority workload for neuro-oncology care providers. Current...

Diffusion-Refined Segmentation and Vision-Language Interpretation for Pediatric Brain Tumor MRI

Accurate pediatric brain tumor segmentation remains challenging due to limited annotated data, heterogeneous imaging phenotypes, diffuse tumor boundar...

Trimodal Glioma Representation Alignment via Volumetric Contrastive Learning

Glioma grading and survival prediction require the integration of heterogeneous information collected at different spatial and biological scales. Hist...

Augmentation techniques for video surveillance in the visible and thermal spectral range

In intelligent video surveillance, cameras record image sequences during day and night. Commonly, this demands different sensors. To achieve a better ...

Multimodal Brain Tumour Classification Using Feature Fusion

Clinicians diagnose brain tumors by synthesizing patient symptoms, medical history, and quantitative imaging data from modalities such as MRI and CT s...

Multimodal Brain Tumour Classification Using Feature Fusion

Clinicians diagnose brain tumors by synthesizing patient symptoms, medical history, and quantitative imaging data from modalities such as MRI and CT s...

Sensitive Glioma Detection and Recurrence Monitoring Using a Machine Learning Model Based on Circulating Monocytes

Background: Non-invasive diagnosis, reliable recurrence surveillance remain critical unmet needs in gliomas. Glioma induces profound systemic immune a...

Redefining Extent Of Resection After Meningioma Surgery: a Multicentre Observational Machine Learning Analysis Comparing Simpson, Radiological and Volumetric Grading

Background: Extent of resection remains central to meningioma management, yet Simpson grading is subjective and may not reflect measurable postoperati...

Multi-Algorithm Machine Learning Benchmarking for Pan-Cancer Classification from Tumour-Educated Platelet RNA Sequencing

Tumour-educated platelets (TEPs) carry cancer-type-specific RNA signatures accessible through whole-blood RNA sequencing, but systematic multi-algorit...

Context-driven Missing-Modality Learning for Robust Medical Diagnosis with Image-Tabular Data

While multimodal data integrating diverse imaging and clinical tabular records is crucial for accurate medical diagnosis, the arbitrary absence of spe...

A quantitative proteomics dataset for assessment and prediction of low dose X-ray radiation exposure in mice.

Ionizing radiation induces molecular responses that may be used to estimate exposure when physical dosimeters are unavailable. Here we present two lar...

Deep Learning for Automated Meningioma Segmentation: Toward Clinical Integration and Workflow Efficiency

Background: Meningiomas are the most common primary intracranial tumors in adults, and volumetric assessment increasingly guides surveillance and trea...

Tsallis-Gated Autoencoder: A Nonextensive Physics-Informed Approach for Unsupervised Anomaly Detection in Glioblastoma Multiforme RNA-seq Data

Glioblastoma multiforme (GBM) is characterised by profound genomic heterogeneity and heavy-tailed gene-expression distributions that challenge convent...

SIGNAL: A Scalable, Real-World Model for Rapid Intraoperative Molecular Classification of Gliomas Using Stimulated Raman Histology

Background: Previous machine learning models to intraoperatively predict the molecular status of gliomas using stimulated Raman histology (SRH), such ...

Predictive Radiomics for Evaluation of Cancer Immune SignaturE in Glioblastoma: the PRECISE-GBM study

Background: Radiogenomics allows identification of radiological biomarkers for genomic phenotypes. In glioblastoma, these biomarkers could potentially...

Entropy Sorting Feature Selection: information-theoretic gene set identification improves single-cell RNA sequencing data interpretability

Single-cell RNA sequencing (scRNA-seq) has transformed our ability to resolve cellular heterogeneity, but extracting meaningful signals remains challe...