Tuberculosis

DualCP: Rehearsal-Free Domain-Incremental Learning via Dual-Level Concept Prototype

Domain-Incremental Learning (DIL) enables vision models to adapt to changing conditions in real-world environments while maintaining the knowledge acquired from previous domains. Given privacy concerns and training time, Rehearsal-Free DIL (RFDIL) is more practical. Inspired by the incremental cognitive process of the human brain, we design Dual-level Concept Prototypes (DualCP) for each class t...

Physics-Guided Multi-Fidelity DeepONet for Data-Efficient Flow Field Prediction

This study presents an enhanced multi-fidelity deep operator network (DeepONet) framework for efficient spatio-temporal flow field prediction, with particular emphasis on practical scenarios where high-fidelity data is scarce. We introduce several key innovations to improve the framework's efficiency and accuracy. First, we enhance the DeepONet architecture by incorporating a merge network that ...

EDiT: Efficient Diffusion Transformers with Linear Compressed Attention

Diffusion Transformers (DiTs) have emerged as a leading architecture for text-to-image synthesis, producing high-quality and photorealistic images. ...

Vision-Language Models for Acute Tuberculosis Diagnosis: A Multimodal Approach Combining Imaging and Clinical Data

Background: This study introduces a Vision-Language Model (VLM) leveraging SIGLIP and Gemma-3b architectures for automated acute tuberculosis (TB) s...

Advancing Chronic Tuberculosis Diagnostics Using Vision-Language Models: A Multi modal Framework for Precision Analysis

Background: This study proposes a Vision-Language Model (VLM) leveraging the SIGLIP encoder and Gemma-3b transformer decoder to enhance automated ch...

GeoRSMLLM: A Multimodal Large Language Model for Vision-Language Tasks in Geoscience and Remote Sensing

The application of Vision-Language Models (VLMs) in remote sensing (RS) has demonstrated significant potential in traditional tasks such as scene cl...

DCAT: Dual Cross-Attention Fusion for Disease Classification in Radiological Images with Uncertainty Estimation

Accurate and reliable image classification is crucial in radiology, where diagnostic decisions significantly impact patient outcomes. Conventional d...

Gaussian RBFNet: Gaussian Radial Basis Functions for Fast and Accurate Representation and Reconstruction of Neural Fields

Neural fields such as DeepSDF and Neural Radiance Fields have recently revolutionized novel-view synthesis and 3D reconstruction from RGB images and...

Attention on the Wires (AttWire): A Foundation Model for Detecting Devices and Catheters in X-ray Fluoroscopic Images

Objective: Interventional devices, catheters and insertable imaging devices such as transesophageal echo (TOE) probes are routinely used in minimall...

Encrypted Vector Similarity Computations Using Partially Homomorphic Encryption: Applications and Performance Analysis

This paper explores the use of partially homomorphic encryption (PHE) for encrypted vector similarity search, with a focus on facial recognition and...

10K is Enough: An Ultra-Lightweight Binarized Network for Infrared Small-Target Detection

The widespread deployment of Infrared Small-Target Detection (IRSTD) algorithms on edge devices necessitates the exploration of model compression te...

A Systematic Review of the Accuracy of Machine Learning Models for Diagnosing Pulmonary Tuberculosis: Implications for Nursing Practice and Implementation.

This systematic review evaluates the application of machine learning (ML) models for diagnosing pulmonary tuberculosis and their potential to inform n...

PaliGemma-CXR: A Multi-task Multimodal Model for TB Chest X-ray Interpretation

Tuberculosis (TB) is a infectious global health challenge. Chest X-rays are a standard method for TB screening, yet many countries face a critical s...

EXACT-CT: EXplainable Analysis for Crohn's and Tuberculosis using CT

Crohn's disease and intestinal tuberculosis share many overlapping features such as clinical, radiological, endoscopic, and histological features - ...

Transforming Tuberculosis Care: Optimizing Large Language Models For Enhanced Clinician-Patient Communication

Tuberculosis (TB) is the leading cause of death from an infectious disease globally, with the highest burden in low- and middle-income countries. In...

An Integrated Deep Learning Framework Leveraging NASNet and Vision Transformer with MixProcessing for Accurate and Precise Diagnosis of Lung Diseases

The lungs are the essential organs of respiration, and this system is significant in the carbon dioxide and exchange between oxygen that occurs in h...

Neural Attention: A Novel Mechanism for Enhanced Expressive Power in Transformer Models

Transformer models typically calculate attention matrices using dot products, which have limitations when capturing nonlinear relationships between ...

Medical Image Classification with KAN-Integrated Transformers and Dilated Neighborhood Attention

Convolutional networks, transformers, hybrid models, and Mamba-based architectures have demonstrated strong performance across various medical image...

Pulmonary Tuberculosis Edge Diagnosis System Based on MindSpore Framework: Low-cost and High-precision Implementation with Ascend 310 Chip

Pulmonary Tuberculosis (PTB) remains a major challenge for global health, especially in areas with poor medical resources, where access to specializ...

Towards Efficient LUT-based PIM: A Scalable and Low-Power Approach for Modern Workloads

Data movement in memory-intensive workloads, such as deep learning, incurs energy costs that are over three orders of magnitude higher than the cost...