Papers Multi-Label Classification
“Multi-Label Classification” 태그가 달린 논문 1,337편 · 필터 해제
Fine-Grained Rib Fracture Diagnosis with Hyperbolic Embeddings: A Detailed Annotation Framework and Multi-Label Classification Model
Accurate rib fracture identification and classification are essential for treatment planning. However, existing datasets often lack fine-grained annotations, particularly regarding rib fracture characterization, type, an…
ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONAFiRe: Anatomy-Driven Self-Supervised Learning for Fine-Grained Representation in Radiographic Images
Current self-supervised methods, such as contrastive learning, predominantly focus on global discrimination, neglecting the critical fine-grained anatomical details required for accurate radiographic analysis. To address…
AnatomyAnomaly DetectionContrastive LearningMulti-Label Classification+2xLSTM-ECG: Multi-label ECG Classification via Feature Fusion with xLSTM
Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, highlighting the critical need for efficient and accurate diagnostic tools. Electrocardiograms (ECGs) are indispensable in diagnosing variou…
ClassificationDiagnosticECG ClassificationMulti-Label Classification+1A Survey of Machine Learning Models and Datasets for the Multi-label Classification of Textual Hate Speech in English
The dissemination of online hate speech can have serious negative consequences for individuals, online communities, and entire societies. This and the large volume of hateful online content prompted both practitioners', …
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONSemantically Encoding Activity Labels for Context-Aware Human Activity Recognition
Prior work has primarily formulated CA-HAR as a multi-label classification problem, where model inputs are time-series sensor data and target labels are binary encodings representing whether a given activity or context o…
Activity RecognitionHuman Activity RecognitionMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Can Reasoning LLMs Enhance Clinical Document Classification?
Clinical document classification is essential for converting unstructured medical texts into standardised ICD-10 diagnoses, yet it faces challenges due to complex medical language, privacy constraints, and limited annota…
ClassificationDocument ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONBeyond Conventional Transformers: The Medical X-ray Attention (MXA) Block for Improved Multi-Label Diagnosis Using Knowledge Distillation
Medical imaging, particularly X-ray analysis, often involves detecting multiple conditions simultaneously within a single scan, making multi-label classification crucial for real-world clinical applications. We present t…
Anomaly DetectionKnowledge DistillationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONBASIR: Budget-Assisted Sectoral Impact Ranking -- A Dataset for Sector Identification and Performance Prediction Using Language Models
Government fiscal policies, particularly annual union budgets, exert significant influence on financial markets. However, real-time analysis of budgetary impacts on sector-specific equity performance remains methodologic…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-label classification for multi-temporal, multi-spatial coral reef condition monitoring using vision foundation model with adapter learning
Coral reef ecosystems provide essential ecosystem services, but face significant threats from climate change and human activities. Although advances in deep learning have enabled automatic classification of coral reef co…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONTransfer LearningImitating Radiological Scrolling: A Global-Local Attention Model for 3D Chest CT Volumes Multi-Label Anomaly Classification
The rapid increase in the number of Computed Tomography (CT) scan examinations has created an urgent need for automated tools, such as organ segmentation, anomaly classification, and report generation, to assist radiolog…
Anomaly ClassificationComputed Tomography (CT)Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Advancing Vulnerability Classification with BERT: A Multi-Objective Learning Model
The rapid increase in cybersecurity vulnerabilities necessitates automated tools for analyzing and classifying vulnerability reports. This paper presents a novel Vulnerability Report Classifier that leverages the BERT (B…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONA Multi-Modal Federated Learning Framework for Remote Sensing Image Classification
Federated learning (FL) enables the collaborative training of deep neural networks across decentralized data archives (i.e., clients) without sharing the local data of the clients. Most of the existing FL methods assume …
Federated Learningimage-classificationImage ClassificationMulti-Label Classification+2Weakly Supervised Convolutional Dictionary Learning for Multi-Label Classification
Convolutional Dictionary Learning (CDL) has emerged as a powerful approach for signal representation by learning translation-invariant features through convolution operations. While existing CDL methods are predominantly…
ClassificationDictionary LearningEnvironmental Sound ClassificationMulti-Label Classification+2On the Generalization of Representation Uncertainty in Earth Observation
Recent advances in Computer Vision have introduced the concept of pretrained representation uncertainty, enabling zero-shot uncertainty estimation. This holds significant potential for Earth Observation (EO), where trust…
Earth ObservationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONSensitivityZero-Shot Multi-Label Classification of Bangla Documents: Large Decoders Vs. Classic Encoders
Bangla, a language spoken by over 300 million native speakers and ranked as the sixth most spoken language worldwide, presents unique challenges in natural language processing (NLP) due to its complex morphological chara…
DecoderMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONLarge Language Models for Healthcare Text Classification: A Systematic Review
Large Language Models (LLMs) have fundamentally transformed approaches to Natural Language Processing (NLP) tasks across diverse domains. In healthcare, accurate and cost-efficient text classification is crucial, whether…
ArticlesBinary ClassificationClassificationMulti-Label Classification+3Artificially Generated Visual Scanpath Improves Multi-label Thoracic Disease Classification in Chest X-Ray Images
Expert radiologists visually scan Chest X-Ray (CXR) images, sequentially fixating on anatomical structures to perform disease diagnosis. An automatic multi-label classifier of diseases in CXR images can benefit by incorp…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONScanpath predictionThoracic Disease ClassificationLotus at SemEval-2025 Task 11: RoBERTa with Llama-3 Generated Explanations for Multi-Label Emotion Classification
This paper presents a novel approach for multi-label emotion detection, where Llama-3 is used to generate explanatory content that clarifies ambiguous emotional expressions, thereby enhancing RoBERTa's emotion classifica…
ClassificationEmotion ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONApplications of wavelet transform in classification of local field potential recorded from the rat brain in conditioned place preference paradigm
This study investigates the multi-label classification of Local Field Potential (LFP) data from the hippocampus (HIP) and nucleus accumbens (NAc) in the rat brain, focusing on reward responses using the Conditioned Place…
ClassificationHippocampusMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONQUAD-LLM-MLTC: Large Language Models Ensemble Learning for Healthcare Text Multi-Label Classification
The escalating volume of collected healthcare textual data presents a unique challenge for automated Multi-Label Text Classification (MLTC), which is primarily due to the scarcity of annotated texts for training and thei…
Computational EfficiencyEnsemble LearningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+5