Lymphoma

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 critical assumption that patients follow the prescribed treatments recorded in EHRs. Using EHR data from 3,623 hypertension patients, we investigate how treatment non-adherence introduces implicit bias that can fundamentally distort both causal inferen...

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 key components: a prover and a distributed verifier. The prover is an all-powerful computational entity capable of performing any Turing-computable operation instantaneously. Its role is to convince the distributed verifier -- composed of the graph's n...

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...

Bounds on the computational complexity of neurons due to dendritic morphology

The simple linear threshold units used in many artificial neural networks have a limited computational capacity. Famously, a single unit cannot handle...

CellFuse Enables Multi-modal Integration of Single-cell and Spatial Proteomics data

Single-cell and spatial proteomic technologies capture complementary biological information, yet no single platform can measure all modalities within ...

Mechanistically Informed Machine Learning Links Non-Canonical TCA Cycle Activity to Warburg Metabolism and Hallmarks of Malignancy

Cancer cells undergo extensive metabolic rewiring to support growth, survival, and phenotypic plasticity. A non-canonical variant of the tricarboxylic...

Quantum Convolutional HLA Immunogenic Peptide Prediction (Q-CHIPP): Next-Generation Neoantigen Prediction with Quantum Neural Networks

The immune system is an intricately evolved series of cellular and protein-protein interactions, which defend the body against pathogens and abnormal ...

Design of peptides with non-canonical amino acids using flow matching

The canonical vocabulary of twenty amino acids limits the chemical space available to proteins and peptides. Expanding this vocabulary to hundreds of ...