Lymphoma

Fully-automated sleep staging: multicenter validation of a generalizable deep neural network for Parkinson's disease and isolated REM sleep behavior disorder

Isolated REM sleep behavior disorder (iRBD) is a key prodromal marker of Parkinson's disease (PD), and video-polysomnography (vPSG) remains the diagnostic gold standard. However, manual sleep staging is particularly challenging in neurodegenerative diseases due to EEG abnormalities and fragmented sleep, making PSG assessments a bottleneck for deploying new RBD screening technologies at scale. We a...

Open-Source Offline-Deployable Retrieval-Augmented Large Language Model for Assisting Pancreatic Cancer Staging

Purpose: Large language models (LLMs) are increasingly applied in radiology, but key challenges remain, including data leakage from cloud-based systems, false outputs, and limited reasoning transparency. This study aimed to develop an open-source, offline-deployable retrieval-augmented LLM (RA-LLM) system in which local execution prevents data leakage and retrieval-augmented generation (RAG) impro...

Predicting Obstetric and Non-obstetric Diagnoses Co-occurrences during Pregnancy

Pregnancy care often involves simultaneous obstetric and other medical conditions, but their co-occurrence patterns are rarely modeled explicitly in a...

SpliceRead: Improving Canonical and Non-Canonical Splice Site Prediction with Residual Blocks and Synthetic Data Augmentation

Accurate splice site prediction is fundamental to understanding gene expression and its associated disorders. However, most existing models are biased...

An explainable framework for the relationship between dementia and glucose metabolism patterns

High-dimensional neuroimaging data presents challenges for assessing neurodegenerative diseases due to complex non-linear relationships. Variational A...

AlphaGenome -enabled analysis of non-coding regulatory variants underlying RHD Expression

Systematic identification of functional non-coding regulatory variants remains a major challenge in human genetics. Conventional approaches such as la...

How does Graph Structure Modulate Membership-Inference Risk for Graph Neural Networks?

Graph neural networks (GNNs) have become the standard tool for encoding data and their complex relationships into continuous representations, improvin...

IMAGENE: Single-cell association of live cell imaging and gene expression profiles of non-adherent cells through photoactivatable adhesives

Live cell imaging is uniquely placed to study cell behavior as it preserves spatial context and enables non-destructive observations over time. Integr...

Spatial Decoding of Tertiary Lymphoid Structure Maturation in Non-Small Cell Lung Cancer Using Deep Neural Networks

Understanding the role of tertiary lymphoid structures (TLS) is crucial in non-small cell lung cancer (NSCLC), as they are associated with patient pro...

A Theoretical Framework for Quantifying Tumour Resistance to Standardized Treatments: A Novel Rudimentary Scalar Mathematical Model with Implications for Breast Cancer Prognosis and Treatment.

BackgroundPrecision oncology relies heavily on genomic profiling and artificial intelligence to predict therapeutic response in breast cancer. However...

Rethinking cell-based neural architecture search: A theoretical perspective.

In this paper, we explore several fundamental theoretical issues in cell-based neural architecture search, including whether different architectures i...

Opposing-through crash risk forecasting using artificial intelligence-based video analytics for real-time application: integrating generalized extreme value theory and time series forecasting models.

Recent advancements in artificial intelligence (AI) and traffic sensing technologies provide significant opportunities for real-time crash risk foreca...

Metabolic pathway alterations in cerebrospinal fluid as diagnostic biomarkers for primary central nervous system lymphoma.

Primary Central Nervous System Lymphoma (PCNSL) is a rare and aggressive type of hematological malignancy that can pose diagnostic challenges. Early d...

An Efficient Approach for Muscle Segmentation and 3D Reconstruction Using Keypoint Tracking in MRI Scan

Magnetic resonance imaging (MRI) enables non-invasive, high-resolution analysis of muscle structures. However, automated segmentation remains limite...

Cross-Modality Masked Learning for Survival Prediction in ICI Treated NSCLC Patients

Accurate prognosis of non-small cell lung cancer (NSCLC) patients undergoing immunotherapy is essential for personalized treatment planning, enablin...

Radar Velocity Transformer: Single-scan Moving Object Segmentation in Noisy Radar Point Clouds

The awareness about moving objects in the surroundings of a self-driving vehicle is essential for safe and reliable autonomous navigation. The inter...

Adaptive Cubic Regularized Second-Order Latent Factor Analysis Model

High-dimensional and incomplete (HDI) data, characterized by massive node interactions, have become ubiquitous across various real-world application...

A robust and versatile deep learning model for prediction of the arterial input function in dynamic small animal $\left[^{18}\text{F}\right]$FDG PET imaging

Dynamic positron emission tomography (PET) and kinetic modeling are pivotal in advancing tracer development research in small animal studies. Accura...

[A deep learning method for differentiating nasopharyngeal carcinoma and lymphoma based on MRI].

To development a deep learning（DL） model based on conventional MRI for automatic segmentation and differential diagnosis of nasopharyngeal carcinoma（N...

Automatic Detection of B-Lines in Lung Ultrasound Based on the Evaluation of Multiple Characteristic Parameters Using Raw RF Data.

B-line artifacts in lung ultrasound, pivotal for diagnosing pulmonary conditions, warrant automated recognition to enhance diagnostic accuracy. In thi...