Lymphoma

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 prognostic assessment of primary central nervous system lymphoma (PCNSL). The present study aimed to develop a multimodal artificial intelligence deep learning segmentation model to address the challenges associated with traditional 2D measurements and manual...

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 will be useful for initiating treatment plans to achieve a greater rate of cure or reduced risk of treatment-related morbidity and death. Numerous research studies have revealed several pre-treatment predictive variables, including age, lactate dehydro...

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,...

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...

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 ...

A Hierarchical Graph Convolutional Network With Infomax-Guided Graph Embedding for Population-Based ASD Detection.

Recently, functional magnetic resonance imaging (fMRI)-based brain networks have been shown to be an effective diagnostic tool with great potential fo...

Multi-class brain malignant tumor diagnosis in magnetic resonance imaging using convolutional neural networks.

Glioblastoma (GBM), primary central nervous system lymphoma (PCNSL), and brain metastases (BM) are common malignant brain tumors with similar radiolog...

A novel artificial intelligence-based methodology to predict non-specific response to treatment.

Non-specific response to treatment (NSRT) is the primary contributor to the failure of randomized clinical trials in major depressive disorder (MDD). ...

Design and molecular mechanism investigation of ALK inhibitors based on virtual screening and structural descriptor modeling.

To address the challenges of target specificity and drug resistance in Anaplastic lymphoma kinase (ALK) inhibition, this study conducted a virtual scr...

Personalized Subgraph Federated Learning with Differentiable Auxiliary Projections

Federated learning (FL) on graph-structured data typically faces non-IID challenges, particularly in scenarios where each client holds a distinct su...

Dual-Task Graph Neural Network for Joint Seizure Onset Zone Localization and Outcome Prediction using Stereo EEG

Accurately localizing the brain regions that triggers seizures and predicting whether a patient will be seizure-free after surgery are vital for sur...

Predicting artificial neural network representations to learn recognition model for music identification from brain recordings.

Recent studies have demonstrated that the representations of artificial neural networks (ANNs) can exhibit notable similarities to cortical representa...