Search Results for author:
Found 53 papers, 9 papers with code
Robust Multimodal Learning for Ophthalmic Disease Grading via Disentangled Representation
The Disentangled Representation Learning module separates multimodal data into modality-common and modality-unique representations, reducing feature entanglement and enhancing both robustness and interpretability in ophthalmic disease diagnosis.
MMRC: A Large-Scale Benchmark for Understanding Multimodal Large Language Model in Real-World Conversation
Recent multimodal large language models (MLLMs) have demonstrated significant potential in open-ended conversation, generating more accurate and personalized responses.
Leveraging Taxonomy and LLMs for Improved Multimodal Hierarchical Classification
Multi-level Hierarchical Classification (MLHC) tackles the challenge of categorizing items within a complex, multi-layered class structure.
Visual question answering: from early developments to recent advances -- a survey
Visual Question Answering (VQA) is an evolving research field aimed at enabling machines to answer questions about visual content by integrating image and language processing techniques such as feature extraction, object detection, text embedding, natural language understanding, and language generation.
How Can LLMs and Knowledge Graphs Contribute to Robot Safety? A Few-Shot Learning Approach
Large Language Models (LLMs) are transforming the robotics domain by enabling robots to comprehend and execute natural language instructions.
GAMED: Knowledge Adaptive Multi-Experts Decoupling for Multimodal Fake News Detection
Multimodal fake news detection often involves modelling heterogeneous data sources, such as vision and language.
Biological Brain Age Estimation using Sex-Aware Adversarial Variational Autoencoder with Multimodal Neuroimages
Combining structural magnetic resonance imaging (sMRI) and functional magnetic resonance imaging (fMRI) has the potential to improve brain age estimation by leveraging complementary data.
ByteScience: Bridging Unstructured Scientific Literature and Structured Data with Auto Fine-tuned Large Language Model in Token Granularity
Natural Language Processing (NLP) is widely used to supply summarization ability from long context to structured information.
Multi-Task Adversarial Variational Autoencoder for Estimating Biological Brain Age with Multimodal Neuroimaging
Despite advances in deep learning for estimating brain age from structural MRI data, incorporating functional MRI data is challenging due to its complex structure and the noisy nature of functional connectivity measurements.
SMILE-UHURA Challenge -- Small Vessel Segmentation at Mesoscopic Scale from Ultra-High Resolution 7T Magnetic Resonance Angiograms
The human brain receives nutrients and oxygen through an intricate network of blood vessels.
SimpsonsVQA: Enhancing Inquiry-Based Learning with a Tailored Dataset
Visual Question Answering (VQA) has emerged as a promising area of research to develop AI-based systems for enabling interactive and immersive learning.
Exploring Capabilities of Time Series Foundation Models in Building Analytics
The growing integration of digitized infrastructure with Internet of Things (IoT) networks has transformed the management and optimization of building energy consumption.
Better to Ask in English: Evaluation of Large Language Models on English, Low-resource and Cross-Lingual Settings
To address this gap, in this study, we evaluate LLMs such as GPT-4, Llama 2, and Gemini to analyze their effectiveness in English compared to other low-resource languages from South Asia (e. g., Bangla, Hindi, and Urdu).
Latent fingerprint enhancement for accurate minutiae detection
Identification of suspects based on partial and smudged fingerprints, commonly referred to as fingermarks or latent fingerprints, presents a significant challenge in the field of fingerprint recognition.
LSSF-Net: Lightweight Segmentation with Self-Awareness, Spatial Attention, and Focal Modulation
However, these models often struggle with capturing the complex and varied characteristics of skin lesions, such as the presence of indistinct boundaries and diverse lesion appearances, which can lead to suboptimal segmentation performance. To address these challenges, we propose a novel lightweight network specifically designed for skin lesion segmentation utilizing mobile devices, featuring a minimal number of learnable parameters (only 0. 8 million).
PitVis-2023 Challenge: Workflow Recognition in videos of Endoscopic Pituitary Surgery
The field of computer vision applied to videos of minimally invasive surgery is ever-growing.
TESL-Net: A Transformer-Enhanced CNN for Accurate Skin Lesion Segmentation
In this study, we introduced a novel network named TESL-Net for the segmentation of skin lesions.
A Robust Algorithm for Contactless Fingerprint Enhancement and Matching
Compared to contact fingerprint images, contactless fingerprint images exhibit four distinct characteristics: (1) they contain less noise; (2) they have fewer discontinuities in ridge patterns; (3) the ridge-valley pattern is less distinct; and (4) they pose an interoperability problem, as they lack the elastic deformation caused by pressing the finger against the capture device.
Do Large Language Models Speak All Languages Equally? A Comparative Study in Low-Resource Settings
Large language models (LLMs) have garnered significant interest in natural language processing (NLP), particularly their remarkable performance in various downstream tasks in resource-rich languages.
Discriminating retinal microvascular and neuronal differences related to migraines: Deep Learning based Crossectional Study
Using CFP type 1 data, discrimination (AUC [95% CI]) was high (0. 84 [0. 8, 0. 88] to 0. 87 [0. 84, 0. 91]) and not significantly different between VGG-16, ResNet-50, and Inceptionv3.
NarrationDep: Narratives on Social Media For Automatic Depression Detection
As a result, \texttt{NarrationDep} is characterized by a novel two-layer deep learning model: the first layer models using social media text posts, and the second layer learns semantic representations of tweets associated with a cluster.
Region Guided Attention Network for Retinal Vessel Segmentation
In this work, we present a lightweight retinal vessel segmentation network based on the encoder-decoder mechanism with region-guided attention.
Vulnerability Detection in Smart Contracts: A Comprehensive Survey
While traditional methods to detect and mitigate vulnerabilities in smart contracts are limited due to a lack of comprehensiveness and effectiveness, integrating advanced machine learning technologies presents an attractive approach to increasing effective vulnerability countermeasures.
LMBF-Net: A Lightweight Multipath Bidirectional Focal Attention Network for Multifeatures Segmentation
Retinal diseases can cause irreversible vision loss in both eyes if not diagnosed and treated early.
BTS: Building Timeseries Dataset: Empowering Large-Scale Building Analytics
Buildings play a crucial role in human well-being, influencing occupant comfort, health, and safety.
Assessment of Left Atrium Motion Deformation Through Full Cardiac Cycle
Unlike Right Atrium (RA), Left Atrium (LA) presents distinctive challenges, including much thinner myocardial walls, complex and irregular morphology, as well as diversity in individual's structure, making off-the-shelf methods designed for the Left Ventricle (LV) may not work in the context of the left atrium.
Advancing Medical Image Segmentation with Mini-Net: A Lightweight Solution Tailored for Efficient Segmentation of Medical Images
Accurate segmentation of anatomical structures and abnormalities in medical images is crucial for computer-aided diagnosis and analysis.
A Gap in Time: The Challenge of Processing Heterogeneous IoT Data in Digitalized Buildings
The increasing demand for sustainable energy solutions has driven the integration of digitalized buildings into the power grid, leveraging Internet-of-Things (IoT) technologies to enhance energy efficiency and operational performance.
BayesJudge: Bayesian Kernel Language Modelling with Confidence Uncertainty in Legal Judgment Prediction
Predicting legal judgments with reliable confidence is paramount for responsible legal AI applications.
From Uncertainty to Trust: Kernel Dropout for AI-Powered Medical Predictions
AI-driven medical predictions with trustworthy confidence are essential for ensuring the responsible use of AI in healthcare applications.
Construction and Application of Materials Knowledge Graph in Multidisciplinary Materials Science via Large Language Model
By implementing network-based algorithms, MKG not only facilitates efficient link prediction but also significantly reduces reliance on traditional experimental methods.
IDoFew: Intermediate Training Using Dual-Clustering in Language Models for Few Labels Text Classification
Although some approaches have attempted to address this problem through single-stage clustering as an intermediate training step coupled with a pre-trained language model, which generates pseudo-labels to improve classification, these methods are often error-prone due to the limitations of the clustering algorithms.
DHGCN: Dynamic Hop Graph Convolution Network for Self-Supervised Point Cloud Learning
Recent works attempt to extend Graph Convolution Networks (GCNs) to point clouds for classification and segmentation tasks.
Feature Enhancer Segmentation Network (FES-Net) for Vessel Segmentation
Diseases such as diabetic retinopathy and age-related macular degeneration pose a significant risk to vision, highlighting the importance of precise segmentation of retinal vessels for the tracking and diagnosis of progression.
DARWIN Series: Domain Specific Large Language Models for Natural Science
To add new capabilities in natural science, enabling the acceleration and enrichment of automation of the discovery process, we present DARWIN, a series of tailored LLMs for natural science, mainly in physics, chemistry, and material science.
Uncovering Political Hate Speech During Indian Election Campaign: A New Low-Resource Dataset and Baselines
The detection of hate speech in political discourse is a critical issue, and this becomes even more challenging in low-resource languages.
The STOIC2021 COVID-19 AI challenge: applying reusable training methodologies to private data
STOIC2021 consisted of a Qualification phase, where participants developed challenge solutions using 2000 publicly available CT scans, and a Final phase, where participants submitted their training methodologies with which solutions were trained on CT scans of 9724 subjects.
Progressive Class-Wise Attention (PCA) Approach for Diagnosing Skin Lesions
Skin cancer holds the highest incidence rate among all cancers globally.
LDMRes-Net: Enabling Efficient Medical Image Segmentation on IoT and Edge Platforms
The results demonstrate the robustness, generalizability, and high segmentation accuracy of LDMRes-Net, positioning it as an efficient tool for accurate and rapid medical image segmentation in diverse clinical applications, particularly on IoT and edge platforms.
Retinal Vessel Segmentation via a Multi-resolution Contextual Network and Adversarial Learning
Timely and affordable computer-aided diagnosis of retinal diseases is pivotal in precluding blindness.
AIROGS: Artificial Intelligence for RObust Glaucoma Screening Challenge
Artificial intelligence (AI) can be used to analyze color fundus photographs (CFPs) in a cost-effective manner, making glaucoma screening more accessible.
A Novel Approach to Train Diverse Types of Language Models for Health Mention Classification of Tweets
Moreover, our analysis shows that adding noise at earlier layers improves models' performance whereas adding noise at intermediate layers deteriorates models' performance.
Improving Health Mentioning Classification of Tweets using Contrastive Adversarial Training
The idea is to learn word representation by its surrounding words and utilize emojis in the text to help improve the classification results.
Adversarial Attacks on Speech Recognition Systems for Mission-Critical Applications: A Survey
Indeed, Adversarial Artificial Intelligence (AI) which refers to a set of techniques that attempt to fool machine learning models with deceptive data, is a growing threat in the AI and machine learning research community, in particular for machine-critical applications.
Distributed Optimization of Graph Convolutional Network using Subgraph Variance
We first propose a graph augmentation-based partition (GAD-Partition) that can divide original graph into augmented subgraphs to reduce communication by selecting and storing as few significant nodes of other processors as possible while guaranteeing the accuracy of the training.
Understanding Information Spreading Mechanisms During COVID-19 Pandemic by Analyzing the Impact of Tweet Text and User Features for Retweet Prediction
These social media platforms enable users to share information with other users who can reshare this information, thus causing this information to spread.
DepressionNet: A Novel Summarization Boosted Deep Framework for Depression Detection on Social Media
Twitter is currently a popular online social media platform which allows users to share their user-generated content.
A Comprehensive Survey on Word Representation Models: From Classical to State-Of-The-Art Word Representation Language Models
In this survey, we explore different word representation models and its power of expression, from the classical to modern-day state-of-the-art word representation language models (LMS).
BioALBERT: A Simple and Effective Pre-trained Language Model for Biomedical Named Entity Recognition
In recent years, with the growing amount of biomedical documents, coupled with advancement in natural language processing algorithms, the research on biomedical named entity recognition (BioNER) has increased exponentially.
Artificial Intelligence in the Battle against Coronavirus (COVID-19): A Survey and Future Research Directions
Artificial intelligence (AI) has been applied widely in our daily lives in a variety of ways with numerous success stories.
Explainable Depression Detection with Multi-Modalities Using a Hybrid Deep Learning Model on Social Media
Model interpretability has become important to engenders appropriate user trust by providing the insight into the model prediction.
Leveraging Big Data Analytics in Healthcare Enhancement: Trends, Challenges and Opportunities
It promises us the power of early detection, prediction, prevention and helps us to improve the quality of life.
