Tuberculosis

HueManity: Probing Fine-Grained Visual Perception in MLLMs

Multimodal Large Language Models (MLLMs) excel at high-level visual reasoning, but their performance on nuanced perceptual tasks remains surprisingly limited. We present HueManity, a benchmark designed to assess visual perception in MLLMs. The dataset comprises 83,850 images featuring two-character alphanumeric strings embedded in Ishihara test style dot patterns, challenging models on precise p...

Chest Disease Detection In X-Ray Images Using Deep Learning Classification Method

In this work, we investigate the performance across multiple classification models to classify chest X-ray images into four categories of COVID-19, pneumonia, tuberculosis (TB), and normal cases. We leveraged transfer learning techniques with state-of-the-art pre-trained Convolutional Neural Networks (CNNs) models. We fine-tuned these pre-trained architectures on a labeled medical x-ray images. ...

Federated learning in low-resource settings: A chest imaging study in Africa -- Challenges and lessons learned

This study explores the use of Federated Learning (FL) for tuberculosis (TB) diagnosis using chest X-rays in low-resource settings across Africa. FL...

MXDOTP: A RISC-V ISA Extension for Enabling Microscaling (MX) Floating-Point Dot Products

Fast and energy-efficient low-bitwidth floating-point (FP) arithmetic is essential for Artificial Intelligence (AI) systems. Microscaling (MX) stand...

SchoenbAt: Rethinking Attention with Polynomial basis

Kernelized attention extends the attention mechanism by modeling sequence correlations through kernel functions, making significant progresses in op...

Artificial Intelligence Powered Audiomics: The Futuristic Biomarker in Pulmonary Medicine - A State-of-the-Art Review.

AI-driven "audiomics" leverages voice and respiratory sounds as non-invasive biomarkers to diagnose and manage pulmonary conditions, including COVID-1...

Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models

Concept Bottleneck Models (CBMs) try to make the decision-making process transparent by exploring an intermediate concept space between the input im...

Karatsuba Algorithm Revisited for 2D Convolution Computation Optimization.

Convolution plays a significant role in many scientific and technological computations, such as artificial intelligence and signal processing. Convolu...

Current Diagnosing Strategies for Mycobacterium tuberculosis and its Drug Resistance: A Review.

Tuberculosis (TB), caused by Mycobacterium tuberculosis (MTB), remains a major global health threat, compounded by the rise of extensively drug-resist...

AdCare-VLM: Leveraging Large Vision Language Model (LVLM) to Monitor Long-Term Medication Adherence and Care

Chronic diseases, including diabetes, hypertension, asthma, HIV-AIDS, epilepsy, and tuberculosis, necessitate rigorous adherence to medication to av...

Enhancing Cochlear Implant Signal Coding with Scaled Dot-Product Attention

Cochlear implants (CIs) play a vital role in restoring hearing for individuals with severe to profound sensorineural hearing loss by directly stimul...

[Serum proteomics and machine learning unveil new diagnostic biomarkers for tuberculosis in adolescents and young adults].

Adolescents and young adults (AYAs) are one of the major populations susceptible to tuberculosis. However, little is known about the unique characteri...

MGS: Markov Greedy Sums for Accurate Low-Bitwidth Floating-Point Accumulation

We offer a novel approach, MGS (Markov Greedy Sums), to improve the accuracy of low-bitwidth floating-point dot products in neural network computati...

PQS (Prune, Quantize, and Sort): Low-Bitwidth Accumulation of Dot Products in Neural Network Computations

We present PQS, which uses three techniques together - Prune, Quantize, and Sort - to achieve low-bitwidth accumulation of dot products in neural ne...

SRVP: Strong Recollection Video Prediction Model Using Attention-Based Spatiotemporal Correlation Fusion

Video prediction (VP) generates future frames by leveraging spatial representations and temporal context from past frames. Traditional recurrent neu...

InteractRank: Personalized Web-Scale Search Pre-Ranking with Cross Interaction Features

Modern search systems use a multi-stage architecture to deliver personalized results efficiently. Key stages include retrieval, pre-ranking, full ra...

DocSAM: Unified Document Image Segmentation via Query Decomposition and Heterogeneous Mixed Learning

Document image segmentation is crucial for document analysis and recognition but remains challenging due to the diversity of document formats and se...

Graph Classification and Radiomics Signature for Identification of Tuberculous Meningitis

Introduction: Tuberculous meningitis (TBM) is a serious brain infection caused by Mycobacterium tuberculosis, characterized by inflammation of the m...

Attention Xception UNet (AXUNet): A Novel Combination of CNN and Self-Attention for Brain Tumor Segmentation

Accurate segmentation of glioma brain tumors is crucial for diagnosis and treatment planning. Deep learning techniques offer promising solutions, bu...

VESTA: A Versatile SNN-Based Transformer Accelerator with Unified PEs for Multiple Computational Layers

Spiking Neural Networks (SNNs) and transformers represent two powerful paradigms in neural computation, known for their low power consumption and ab...