Lymphoma

Graph neural networks for single-cell omics data: a review of approaches and applications.

Rapid advancement of sequencing technologies now allows for the utilization of precise signals at single-cell resolution in various omics studies. However, the massive volume, ultra-high dimensionality, and high sparsity nature of single-cell data have introduced substantial difficulties to traditional computational methods. The intricate non-Euclidean networks of intracellular and intercellular s...

Explainable Classifier for Malignant Lymphoma Subtyping via Cell Graph and Image Fusion

Malignant lymphoma subtype classification directly impacts treatment strategies and patient outcomes, necessitating classification models that achieve both high accuracy and sufficient explainability. This study proposes a novel explainable Multi-Instance Learning (MIL) framework that identifies subtype-specific Regions of Interest (ROIs) from Whole Slide Images (WSIs) while integrating cell dis...

A Transfer Framework for Enhancing Temporal Graph Learning in Data-Scarce Settings

Dynamic interactions between entities are prevalent in domains like social platforms, financial systems, healthcare, and e-commerce. These interacti...

AMPLE: Event-Driven Accelerator for Mixed-Precision Inference of Graph Neural Networks

Graph Neural Networks (GNNs) have recently gained attention due to their performance on non-Euclidean data. The use of custom hardware architectures...

Asymptotics of Non-Convex Generalized Linear Models in High-Dimensions: A proof of the replica formula

The analytic characterization of the high-dimensional behavior of optimization for Generalized Linear Models (GLMs) with Gaussian data has been a ce...

Revealing Treatment Non-Adherence Bias in Clinical Machine Learning Using Large Language Models

Machine learning systems trained on electronic health records (EHRs) increasingly guide treatment decisions, but their reliability depends on the cr...

Strong and Hiding Distributed Certification of $k$-Coloring

A locally checkable proof (LCP) is a non-deterministic distributed algorithm designed to verify global properties of a graph $G$. It involves two ke...

Are the Majority of Public Computational Notebooks Pathologically Non-Executable?

Computational notebooks are the de facto platforms for exploratory data science, offering an interactive programming environment where users can cre...

Robot Character Generation and Adaptive Human-Robot Interaction with Personality Shaping

We present a novel framework for designing emotionally agile robots with dynamic personalities and memory-based learning, with the aim of performing...

Application of Generative Adversarial Network (GAN) for Synthetic Training Data Creation to improve performance of ANN Classifier for extracting Built-Up pixels from Landsat Satellite Imagery

Training a neural network for pixel based classification task using low resolution Landsat images is difficult as the size of the training data is u...

A Hybrid Deep Learning CNN Model for Enhanced COVID-19 Detection from Computed Tomography (CT) Scan Images

Early detection of COVID-19 is crucial for effective treatment and controlling its spread. This study proposes a novel hybrid deep learning model fo...

Additive Manufacturing Processes Protocol Prediction by Artificial Intelligence using X-ray Computed Tomography data

The quality of the part fabricated from the Additive Manufacturing (AM) process depends upon the process parameters used, and therefore, optimizatio...

Leveraging Multiphase CT for Quality Enhancement of Portal Venous CT: Utility for Pancreas Segmentation

Multiphase CT studies are routinely obtained in clinical practice for diagnosis and management of various diseases, such as cancer. However, the CT ...

Patch-Based and Non-Patch-Based inputs Comparison into Deep Neural Models: Application for the Segmentation of Retinal Diseases on Optical Coherence Tomography Volumes

Worldwide, sight loss is commonly occurred by retinal diseases, with age-related macular degeneration (AMD) being a notable facet that affects elder...

A Learnt Half-Quadratic Splitting-Based Algorithm for Fast and High-Quality Industrial Cone-beam CT Reconstruction

Industrial X-ray cone-beam CT (XCT) scanners are widely used for scientific imaging and non-destructive characterization. Industrial CBCT scanners u...

Multi-stage intermediate fusion for multimodal learning to classify non-small cell lung cancer subtypes from CT and PET

Accurate classification of histological subtypes of non-small cell lung cancer (NSCLC) is essential in the era of precision medicine, yet current in...

Transfer Learning Strategies for Pathological Foundation Models: A Systematic Evaluation in Brain Tumor Classification

Foundation models pretrained on large-scale pathology datasets have shown promising results across various diagnostic tasks. Here, we present a syst...

Am I Infected? Lessons from Operating a Large-Scale IoT Security Diagnostic Service

There is an expectation that users of home IoT devices will be able to secure those devices, but they may lack information about what they need to d...

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis

Multi-modality magnetic resonance imaging (MRI) is essential for the diagnosis and treatment of brain tumors. However, missing modalities are common...

Advancing precision medicine: the transformative role of artificial intelligence in immunogenomics, radiomics, and pathomics for biomarker discovery and immunotherapy optimization.

Artificial intelligence (AI) is significantly advancing precision medicine, particularly in the fields of immunogenomics, radiomics, and pathomics. In...