Lymphoma

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 applications. Second-order latent factor models have shown promising performance in modeling this type of data. Nevertheless, due to the bilinear and non-convex nature of the SLF model's objective function, incorporating a damping term into the Hessian approxim...

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. Accurate kinetic modeling requires precise input function estimation, traditionally achieved via arterial blood sampling. However, arterial cannulation in small animals like mice, involves intricate, time-consuming, and terminal procedures, precluding long...

[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...

Establishment and evaluation of an automatic multi?sequence MRI segmentation model of primary central nervous system lymphoma based on the nnU?Net deep learning network method.

Accurate quantitative assessment using gadolinium-contrast magnetic resonance imaging (MRI) is crucial in therapy planning, surveillance and prognosti...

Artificial intelligence based malignant lymphoma type prediction using enhanced super resolution image and hybrid feature extraction algorithm.

In the medical field, the most common and frequent type of blood cancer is lymphoma. Accurately predicting and early response to lymphoma treatment wi...

StoCFL: A stochastically clustered federated learning framework for Non-IID data with dynamic client participation.

Federated learning is a distributed learning framework that takes full advantage of private data samples kept on edge devices. In real-world federated...

Supervised Diffusion-Model-Based PET Image Reconstruction

Diffusion models (DMs) have recently been introduced as a regularizing prior for PET image reconstruction, integrating DMs trained on high-quality P...

Constructing Non-Markovian Decision Process via History Aggregator

In the domain of algorithmic decision-making, non-Markovian dynamics manifest as a significant impediment, especially for paradigms such as Reinforc...

On Universality of Non-Separable Approximate Message Passing Algorithms

Mean-field characterizations of first-order iterative algorithms -- including Approximate Message Passing (AMP), stochastic and proximal gradient de...

Exploring Non-contrastive Self-supervised Representation Learning for Image-based Profiling

Image-based cell profiling aims to create informative representations of cell images. This technique is critical in drug discovery and has greatly a...

Machine learning-assisted Fourier transform infrared spectroscopy to predict adulteration in coriander powder.

Coriander is a widely used spice, valued for its flavor, aroma, and nutritional benefits in various cuisines and food products. However, adulteration,...

Artificial intelligence approaches for tumor phenotype stratification from single-cell transcriptomic data.

Single-cell RNA-sequencing (scRNA-seq) coupled with robust computational analysis facilitates the characterization of phenotypic heterogeneity within ...

Sleep Stage Classification using Multimodal Embedding Fusion from EOG and PSM

Accurate sleep stage classification is essential for diagnosing sleep disorders, particularly in aging populations. While traditional polysomnograph...

Dynamics and Control of Vision-Aided Multi-UAV-tethered Netted System Capturing Non-Cooperative Target

As the number of Unmanned Aerial Vehicles (UAVs) operating in low-altitude airspace continues to increase, non-cooperative targets pose growing chal...

MIND: Material Interface Generation from UDFs for Non-Manifold Surface Reconstruction

Unsigned distance fields (UDFs) are widely used in 3D deep learning due to their ability to represent shapes with arbitrary topology. While prior wo...

Boolean-network simplification and rule fitting to unravel chemotherapy resistance in non-small cell lung cancer

Boolean networks are powerful frameworks for capturing the logic of gene-regulatory circuits, yet their combinatorial explosion hampers exhaustive a...

A Comprehensive Exploration of Groundwater Quality of Ambagarh Chowki Region, Chhattisgarh, India: Water Quality Index, Health Risk, and ANN Predictive Modeling.

Access to safe and clean drinking water remains a critical global challenge, with groundwater as a primary source for billions of people. Further, tox...

TIGPR: A multi-view ground penetrating radar detection data for damage assessment of transportation infrastructure.

The TIGPR dataset is a high-quality collection of ground-penetrating radar (GPR) images designed for the detection and assessment of transportation in...

Explainable PET-based intratumoral and peritumoral machine learning model for predicting visceral pleural invasion in clinical-stage IA non-small cell lung cancer: A two-center study.

AIM: The aim of this study was to develop a PET-based machine learning model for predicting visceral pleural invasion (VPI) in patients with clinical ...