arXiv subjects

Erik Enriquez

Publications and source records attributed to Erik Enriquez.

6 recordsLinked to original sources

arXiv2026

Longitudinal Bayesian Learning of Continuous Disease Position across the Alzheimer's Disease Continuum

Alzheimer's disease (AD) progresses as a continuous biological process, whereas most existing neuroimaging-based artificial intelligence methods remain limited to discrete diagnosis or clinical score prediction from cross-sectional imaging. In this work, we propose Disease Continuum Positioning (DCP), a longitudinal Bayesian Learning framework that continuously estimates disease severity from longitudinal diffusion tensor imaging (DTI). Specifically, DCP models disease severity as a low-dimensional probabilistic latent variable by jointly integrating longitudinal observations with weak clinical supervision, from which the proposed Disease Continuum Score (DCS) is derived to quantify an individual's position along the Alzheimer's disease continuum together with its associated uncertainty. Extensive experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort demonstrate that DCP consistently outperforms representative disease progression methods. More importantly, comprehensive validation analyses show that DCS accurately characterizes disease severity, exhibits strong clinical relevance, preserves longitudinal disease evolution, and predicts future disease conversion. These results suggest that DCS provides a quantitative imaging-derived representation for continuous assessment of Alzheimer's disease progression beyond conventional diagnostic labels and clinical scores.

cs.LG↗

arXiv2026

Patch-MoE Mamba: A Patch-Ordered Mixture-of-Experts State Space Architecture for Medical Image Segmentation

CNN- and Transformer-based architectures have achieved strong performance in medical image segmentation, but CNNs are limited in modeling long-range dependencies, while Transformers often suffer from quadratic computational and memory complexity. State space models, especially Mamba-based networks, offer an efficient alternative with linear sequence complexity. However, existing Mamba segmentation models still face two limitations: pixel-wise directional scanning can disrupt local 2D spatial structure, and simple summation-based fusion of scan directions cannot adapt well to diverse object sizes, shapes, and boundaries. To address these issues, we propose \textit{Patch-MoE Mamba}, a patch-ordered mixture-of-experts state space architecture for medical image segmentation. It introduces a hierarchical patch-ordered scanning mechanism that preserves local spatial neighborhoods while capturing multi-scale context, and an MoE-based directional fusion module that adaptively combines multiple Mamba scanner outputs using four directional experts, a learnable concatenation expert, and residual directional aggregation. Experiments on five public polyp segmentation benchmarks and the ISIC 2017/2018 skin lesion segmentation datasets demonstrate the effectiveness and generality of Patch-MoE Mamba.

cs.CV↗

arXiv2025

Integrating Multi-scale and Multi-filtration Topological Features for Medical Image Classification

Modern deep neural networks have shown remarkable performance in medical image classification. However, such networks either emphasize pixel-intensity features instead of fundamental anatomical structures (e.g., those encoded by topological invariants), or they capture only simple topological features via single-parameter persistence. In this paper, we propose a new topology-guided classification framework that extracts multi-scale and multi-filtration persistent topological features and integrates them into vision classification backbones. For an input image, we first compute cubical persistence diagrams (PDs) across multiple image resolutions/scales. We then develop a ``vineyard'' algorithm that consolidates these PDs into a single, stable diagram capturing signatures at varying granularities, from global anatomy to subtle local irregularities that may indicate early-stage disease. To further exploit richer topological representations produced by multiple filtrations, we design a cross-attention-based neural network that directly processes the consolidated final PDs. The resulting topological embeddings are fused with feature maps from CNNs or Transformers. By integrating multi-scale and multi-filtration topologies into an end-to-end architecture, our approach enhances the model's capacity to recognize complex anatomical structures. Evaluations on three public datasets show consistent, considerable improvements over strong baselines and state-of-the-art methods, demonstrating the value of our comprehensive topological perspective for robust and interpretable medical image classification.

cs.CV↗

arXiv2025

Modeling Causal Interactions Across Brain Functional Subnetworks for Population-specific Disease Analysis

Current neuroimaging studies on neurodegenerative diseases and psychological risk factors have been developed predominantly in non Hispanic White cohorts, with other populations markedly underrepresented. In this work, we construct directed hyper connectomes among large scale functional brain systems based on causal influences between brain regions, and examine their links to Alzheimer Disease progression and worry levels across racial groups. By using Health and Aging Brain Study Health Disparities (HABS HD) dataset, our experimental results suggest that neglecting racial variation in brain network architecture may reduce predictive performance in both cognitive and affective phenotypes. Important shared and population-specific hyper-connectome patterns related to both AD progression and worry levels were identified. We further observed distinct closed loop directed circuits across groups, suggesting that different populations may rely on distinct feedback based network regulation strategies when supporting cognition or managing emotional states. Together, these results indicate a common backbone of network vulnerability with population-dependent variations in regulatory coordination, underscoring the importance of population-aware neuroimaging models.

q-bio.NC↗

arXiv2023

IntelliBeeHive: An Automated Honey Bee, Pollen, and Varroa Destructor Monitoring System

Utilizing computer vision and the latest technological advancements, in this study, we developed a honey bee monitoring system that aims to enhance our understanding of Colony Collapse Disorder, honey bee behavior, population decline, and overall hive health. The system is positioned at the hive entrance providing real-time data, enabling beekeepers to closely monitor the hive's activity and health through an account-based website. Using machine learning, our monitoring system can accurately track honey bees, monitor pollen-gathering activity, and detect Varroa mites, all without causing any disruption to the honey bees. Moreover, we have ensured that the development of this monitoring system utilizes cost-effective technology, making it accessible to apiaries of various scales, including hobbyists, commercial beekeeping businesses, and researchers. The inference models used to detect honey bees, pollen, and mites are based on the YOLOv7-tiny architecture trained with our own data. The F1-score for honey bee model recognition is 0.95 and the precision and recall value is 0.981. For our pollen and mite object detection model F1-score is 0.95 and the precision and recall value is 0.821 for pollen and 0.996 for "mite". The overall performance of our IntelliBeeHive system demonstrates its effectiveness in monitoring the honey bee's activity, achieving an accuracy of 96.28 % in tracking and our pollen model achieved a F1-score of 0.831.

cs.CV↗

arXiv2016

Conducting interface in oxide homojunction: understanding of superior properties in black TiO2

Black TiO2 nanoparticles with a crystalline-core and amorphous-shell structure exhibit superior optoelectronic properties in comparison with pristine TiO2. The fundamental mechanisms underlying these enhancements, however, remain unclear, largely due to the inherent complexities and limitations of powder materials. Here, we fabricate TiO2 homojunction films consisting of an oxygen-deficient amorphous layer on top of a highly crystalline layer, to simulate the structural/functional configuration of black TiO2 nanoparticles. Metallic conduction is achieved at the crystalline-amorphous homointerface via electronic interface reconstruction, which we show to be the main reason for the superior properties of black TiO2. This work not only achieves an unprecedented understanding of black TiO2, but also provides a new perspective for investigating carrier generation and transport behavior at oxide interfaces, which are of tremendous fundamental and technological interest.

cond-mat.mtrl-sci↗