Cover Image

Effective-LDAM: An Effective Loss Function To Mitigate Data Imbalance for Robust Chest X-Ray Disease Classification

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	S, Sree Rama Vamsidhar, Satya, Bhargava, Gorthi, Rama Krishna
Format:	Preprint
Published:	2024
Subjects:	Image and Video Processing Computer Vision and Pattern Recognition
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

NSSR-DIL: Null-Shot Image Super-Resolution Using Deep Identity Learning
by: S, Sree Rama Vamsidhar, et al.
Published: (2024)

Encoding of Demographic and Anatomical Information in Chest X-Ray-based Severe Left Ventricular Hypertrophy Classifiers
by: Pal, Basudha, et al.
Published: (2025)

Deep Learning-Powered Classification of Thoracic Diseases in Chest X-Rays
by: Lei, Yiming, et al.
Published: (2025)

Evaluating and Improving the Effectiveness of Synthetic Chest X-Rays for Medical Image Analysis
by: Prakash, Eva, et al.
Published: (2024)

Few-shot learning for COVID-19 Chest X-Ray Classification with Imbalanced Data: An Inter vs. Intra Domain Study
by: Galán-Cuenca, Alejandro, et al.
Published: (2024)

Loss Design and Architecture Selection for Long-Tailed Multi-Label Chest X-Ray Classification
by: Sulake, Nikhileswara Rao
Published: (2026)

MedImageInsight for Thoracic Cavity Health Classification from Chest X-rays
by: Boya, Rama Krishna, et al.
Published: (2025)

BS-Diff: Effective Bone Suppression Using Conditional Diffusion Models from Chest X-Ray Images
by: Chen, Zhanghao, et al.
Published: (2023)

Artificially Generated Visual Scanpath Improves Multi-label Thoracic Disease Classification in Chest X-Ray Images
by: Verma, Ashish, et al.
Published: (2025)

BS-LDM: Effective Bone Suppression in High-Resolution Chest X-Ray Images with Conditional Latent Diffusion Models
by: Sun, Yifei, et al.
Published: (2024)

Novel Pooling-based VGG-Lite for Pneumonia and Covid-19 Detection from Imbalanced Chest X-Ray Datasets
by: Roy, Santanu, et al.
Published: (2025)

CALF: A Conditionally Adaptive Loss Function to Mitigate Class-Imbalanced Segmentation
by: Alam, Bashir, et al.
Published: (2025)

Chest Disease Detection In X-Ray Images Using Deep Learning Classification Method
by: Hazlett, Alanna, et al.
Published: (2025)

AttCDCNet: Attention-enhanced Chest Disease Classification using X-Ray Images
by: Khater, Omar Hesham, et al.
Published: (2024)

Explainable Lung Disease Classification from Chest X-Ray Images Utilizing Deep Learning and XAI
by: Ifty, Tanzina Taher, et al.
Published: (2024)

CBAM-Enhanced DenseNet121 for Multi-Class Chest X-Ray Classification with Grad-CAM Explainability
by: Dey, Utsho Kumar
Published: (2026)

SharpXR: Structure-Aware Denoising for Pediatric Chest X-Rays
by: Abolade, Ilerioluwakiiye, et al.
Published: (2025)

Advance Warning Methodologies for COVID-19 using Chest X-Ray Images
by: Ahishali, Mete, et al.
Published: (2020)

Few-Shot Learning Approach on Tuberculosis Classification Based on Chest X-Ray Images
by: Pramana, A. A. G. Yogi, et al.
Published: (2024)

Chest X-Rays Image Classification from beta-Variational Autoencoders Latent Features
by: Crespi, Leonardo, et al.
Published: (2021)

3D CBCT Challenge 2024: Improved Cone Beam CT Reconstruction using SwinIR-Based Sinogram and Image Enhancement
by: Alavala, Sasidhar, et al.
Published: (2024)

Anatomic Feature Fusion Model for Diagnosing Calcified Pulmonary Nodules on Chest X-Ray
by: Choi, Hyeonjin, et al.
Published: (2025)

Partial Decoder Attention Network with Contour-weighted Loss Function for Data-Imbalance Medical Image Segmentation
by: Huang, Zhengyong, et al.
Published: (2026)

InfLocNet: Enhanced Lung Infection Localization and Disease Detection from Chest X-Ray Images Using Lightweight Deep Learning
by: Miah, Md. Asiful Islam, et al.
Published: (2024)

Zero-Shot Pediatric Tuberculosis Detection in Chest X-Rays using Self-Supervised Learning
by: Capellán-Martín, Daniel, et al.
Published: (2024)

FusionRF: High-Fidelity Satellite Neural Radiance Fields from Multispectral and Panchromatic Acquisitions
by: Sprintson, Michael, et al.
Published: (2024)

Diagnosing COVID-19 Severity from Chest X-Ray Images Using ViT and CNN Architectures
by: Lara, Luis, et al.
Published: (2025)

D3: Deep Deconvolution Deblurring for Natural Images
by: Saraswathula, Vamsidhar, et al.
Published: (2024)

Reproducing and Improving CheXNet: Deep Learning for Chest X-ray Disease Classification
by: Strick, Daniel J., et al.
Published: (2025)

Comprehensive Lung Disease Detection Using Deep Learning Models and Hybrid Chest X-ray Data with Explainable AI
by: Sarker, Shuvashis, et al.
Published: (2025)

Advanced Chest X-Ray Analysis via Transformer-Based Image Descriptors and Cross-Model Attention Mechanism
by: Agarwal, Lakshita, et al.
Published: (2025)

MultiFusionNet: Multilayer Multimodal Fusion of Deep Neural Networks for Chest X-Ray Image Classification
by: Agarwal, Saurabh, et al.
Published: (2024)

Cardiovascular Disease Detection from Multi-View Chest X-rays with BI-Mamba
by: Yang, Zefan, et al.
Published: (2024)

Privacy-Preserving Chest X-ray Classification in Latent Space with Homomorphically Encrypted Neural Inference
by: Kim, Jonghun, et al.
Published: (2025)

Prompt2SegCXR:Prompt to Segment All Organs and Diseases in Chest X-rays
by: Zami, Abduz, et al.
Published: (2025)

GL-LCM: Global-Local Latent Consistency Models for Fast High-Resolution Bone Suppression in Chest X-Ray Images
by: Sun, Yifei, et al.
Published: (2025)

Classification of COVID-19 in Chest X-ray Images Using Fusion of Deep Features and LightGBM
by: Nasiri, Hamid, et al.
Published: (2022)

From Isolation to Collaboration: Federated Class-Heterogeneous Learning for Chest X-Ray Classification
by: Kulkarni, Pranav, et al.
Published: (2023)

Multi-pathology Chest X-ray Classification with Rejection Mechanisms
by: Aperstein, Yehudit, et al.
Published: (2025)

Exploring Foundation Models for Synthetic Medical Imaging: A Study on Chest X-Rays and Fine-Tuning Techniques
by: da Silva, Davide Clode, et al.
Published: (2024)