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

arXiv: https://arxiv.org/abs/2407.04953 · HTML: https://mlsanity.com/paper/2407.04953/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 29 references that are in the map.
- First posted on arXiv 6 Jul 2024.

## Builds on (most cited first, 15 of 15)

- [Class-Balanced Loss Based on Effective Number of Samples](https://mlsanity.com/paper/1901.05555/) (2019)
- [Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss](https://mlsanity.com/paper/1906.07413/) (2019)
- [CosFace: Large Margin Cosine Loss for Deep Face Recognition](https://mlsanity.com/paper/1801.09414/) (2018)
- [SphereFace: Deep Hypersphere Embedding for Face Recognition](https://mlsanity.com/paper/1704.08063/) (2017)
- [A systematic study of the class imbalance problem in convolutional neural networks](https://mlsanity.com/paper/1710.05381/) (2017)
- [The Effectiveness of Data Augmentation in Image Classification using Deep Learning](https://mlsanity.com/paper/1712.04621/) (2017)
- [Large-Margin Softmax Loss for Convolutional Neural Networks](https://mlsanity.com/paper/1612.02295/) (2016)
- [Additive Margin Softmax for Face Verification](https://mlsanity.com/paper/1801.05599/) (2018)
- [COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images](https://mlsanity.com/paper/2003.09871/) (2020)
- [Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning](https://mlsanity.com/paper/1806.06193/) (2018)
- [M2m: Imbalanced Classification via Major-to-minor Translation](https://mlsanity.com/paper/2004.00431/) (2020)
- [Striking the Right Balance with Uncertainty](https://mlsanity.com/paper/1901.07590/) (2019)
- [Overfitting of neural nets under class imbalance: Analysis and improvements for segmentation](https://mlsanity.com/paper/1907.10982/) (2019)
- [A Deep Convolutional Neural Network for COVID-19 Detection Using Chest X-Rays](https://mlsanity.com/paper/2005.01578/) (2020)
- [Robust Screening of COVID-19 from Chest X-ray via Discriminative Cost-Sensitive Learning](https://mlsanity.com/paper/2004.12592/) (2020)

## Built on by (most cited first, 1 of 1)

- [Uncertainty-Guided Label Rebalancing for CPS Safety Monitoring](https://mlsanity.com/paper/2603.25670/) (2026)

## Abstract (arXiv)

Deep Learning (DL) approaches have gained prominence in medical imaging for disease diagnosis. Chest X-ray (CXR) classification has emerged as an effective method for detecting various diseases. Among these methodologies, Chest X-ray (CXR) classification has proven to be an effective approach for detecting and analyzing various diseases. However, the reliable performance of DL classification algorithms is dependent upon access to large and balanced datasets, which pose challenges in medical imaging due to the impracticality of acquiring sufficient data for every disease category. To tackle this problem, we propose an algorithmic-centric approach called Effective-Label Distribution Aware Margin (E-LDAM), which modifies the margin of the widely adopted Label Distribution Aware Margin (LDAM) loss function using an effective number of samples in each class. Experimental evaluations on the COVIDx CXR dataset focus on Normal, Pneumonia, and COVID-19 classification. The experimental results demonstrate the effectiveness of the proposed E-LDAM approach, achieving a remarkable recall score of 97.81% for the minority class (COVID-19) in CXR image prediction. Furthermore, the overall accuracy of the three-class classification task attains an impressive level of 95.26%.