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Unlike parametric methods, nonparametric statistics are ideal for nominal and ordinal data, requiring fewer assumptions about the population's nature or distribution. This makes nonparametric methods easier to apply and interpret, as they do not depend on parameters like mean or standard deviation. One common approach in nonparametric analysis is to sort data according to a specific criterion. For instance, we might arrange weather data from hottest to coldest days in a month or rank cities...
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  1. Home
  3. Enhancing Multi-label Chest X-ray Classification Using An Improved Ranking Loss.
  1. Home
  3. Enhancing Multi-label Chest X-ray Classification Using An Improved Ranking Loss.

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Enhancing Multi-Label Chest X-Ray Classification Using an Improved Ranking Loss.

Muhammad Shehzad Hanif1, Muhammad Bilal1, Abdullah H Alsaggaf2

  • 1Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia.

Bioengineering (Basel, Switzerland)
|June 26, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a new focal ZLPR loss function to improve multi-label classification of thoracic diseases in chest X-ray images, effectively handling class imbalance and enhancing diagnostic accuracy.

Keywords:
chest X-rayconvolutional neural networkslearning by rankingmulti-label classification

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Chest X-ray (CXR) analysis for thoracic diseases presents a multi-label classification challenge due to co-occurring pathologies.
  • Class imbalance in CXR datasets, where some diseases are rare, complicates accurate model training.
  • Training deep learning models from scratch requires massive datasets, often unavailable for specific medical tasks.

Purpose of the Study:

  • To develop an effective method for multi-label classification of thoracic diseases in CXR images.
  • To address the challenge of class imbalance in CXR datasets.
  • To improve the performance of deep learning models in thoracic disease detection.

Main Methods:

  • Utilized transfer learning by fine-tuning a pretrained DenseNet121 model on the NIH Chest X-ray14 dataset.
  • Proposed a novel rank-based loss function, focal ZLPR (FZLPR), inspired by focal loss to address class imbalance.
  • Incorporated a temperature parameter in FZLPR to emphasize hard-to-classify instances of rare diseases.

    • Main Results:

    • The proposed FZLPR loss function outperformed standard loss functions like binary cross entropy (BCE) and focal loss.
    • Models trained with FZLPR demonstrated superior performance on the NIH Chest X-ray14 dataset.
    • Achieved a competitive average AUC of 80.96% using test-time augmentations with the FZLPR-trained model.

    Conclusions:

    • The focal ZLPR loss function is a promising approach for improving multi-label classification of thoracic diseases in CXR images.
    • The methodology effectively handles class imbalance, leading to more accurate detection of both common and rare diseases.
    • The developed model shows competitive performance, offering potential for enhanced diagnostic support in clinical settings.