Clinically-Inspired Hierarchical Multi-Label Classification of Chest X-rays with a Penalty-Based Loss Function

• URL: http://arxiv.org/abs/2502.03591v1
• Date: Wed, 05 Feb 2025 20:15:06 GMT
• Title: Clinically-Inspired Hierarchical Multi-Label Classification of Chest X-rays with a Penalty-Based Loss Function
• Authors: Mehrdad Asadi, Komi Sodoké, Ian J. Gerard, Marta Kersten-Oertel,
• Abstract summary: We present a novel approach to multi-label chest X-ray (CXR) image classification that enhances clinical interpretability. We incorporate hierarchical label groupings to capture clinically meaningful relationships between diagnoses. Our model achieved a mean area under the receiver operating characteristic curve (AUROC) of 0.903 on the test set.
• Score: 1.264536505250038
• License:
• Abstract: In this work, we present a novel approach to multi-label chest X-ray (CXR) image classification that enhances clinical interpretability while maintaining a streamlined, single-model, single-run training pipeline. Leveraging the CheXpert dataset and VisualCheXbert-derived labels, we incorporate hierarchical label groupings to capture clinically meaningful relationships between diagnoses. To achieve this, we designed a custom hierarchical binary cross-entropy (HBCE) loss function that enforces label dependencies using either fixed or data-driven penalty types. Our model achieved a mean area under the receiver operating characteristic curve (AUROC) of 0.903 on the test set. Additionally, we provide visual explanations and uncertainty estimations to further enhance model interpretability. All code, model configurations, and experiment details are made available.

Related papers

• Chest X-ray Foundation Model with Global and Local Representations Integration [13.736829173377355]
  CheXFound is a vision foundation model that learns robust CXR representations and generalizes effectively across a wide range of downstream tasks. We pretrain CheXFound on a curated CXR-1M dataset, comprising over one million unique CXRs from publicly available sources. Our experimental results show that CheXFound outperforms state-of-the-art models in classifying 40 disease findings across different prevalence levels.
  arXiv  Detail & Related papers  (2025-02-07T18:16:15Z)
• How Can We Tame the Long-Tail of Chest X-ray Datasets? [0.0]
  Chest X-rays (CXRs) are a medical imaging modality that is used to infer a large number of abnormalities. Few of them are quite commonly observed and are abundantly represented in CXR datasets. It is challenging for current models to learn independent discriminatory features for labels that are rare but may be of high significance.
  arXiv  Detail & Related papers  (2023-09-08T12:28:40Z)
• How Does Pruning Impact Long-Tailed Multi-Label Medical Image Classifiers? [49.35105290167996]
  Pruning has emerged as a powerful technique for compressing deep neural networks, reducing memory usage and inference time without significantly affecting overall performance. This work represents a first step toward understanding the impact of pruning on model behavior in deep long-tailed, multi-label medical image classification.
  arXiv  Detail & Related papers  (2023-08-17T20:40:30Z)
• Learning disentangled representations for explainable chest X-ray classification using Dirichlet VAEs [68.73427163074015]
  This study explores the use of the Dirichlet Variational Autoencoder (DirVAE) for learning disentangled latent representations of chest X-ray (CXR) images. The predictive capacity of multi-modal latent representations learned by DirVAE models is investigated through implementation of an auxiliary multi-label classification task.
  arXiv  Detail & Related papers  (2023-02-06T18:10:08Z)
• Improving Classification Model Performance on Chest X-Rays through Lung Segmentation [63.45024974079371]
  We propose a deep learning approach to enhance abnormal chest x-ray (CXR) identification performance through segmentations. Our approach is designed in a cascaded manner and incorporates two modules: a deep neural network with criss-cross attention modules (XLSor) for localizing lung region in CXR images and a CXR classification model with a backbone of a self-supervised momentum contrast (MoCo) model pre-trained on large-scale CXR data sets.
  arXiv  Detail & Related papers  (2022-02-22T15:24:06Z)
• Severity Quantification and Lesion Localization of COVID-19 on CXR using Vision Transformer [25.144248675578286]
  Under the global pandemic of COVID-19, building an automated framework that quantifies the severity of COVID-19 has become increasingly important. We propose a novel Vision Transformer tailored for both quantification of the severity and clinically applicable localization of the COVID-19 related lesions. Our model is trained in a weakly-supervised manner to generate the full probability maps from weak array-based labels.
  arXiv  Detail & Related papers  (2021-03-12T03:17:19Z)
• Deep Hiearchical Multi-Label Classification Applied to Chest X-Ray Abnormality Taxonomies [26.841289081747036]
  We present a deep HMLC approach for CXR CAD. We show that first training the network to model conditional probability directly and then refining it with unconditional probabilities is key in boosting performance. We demonstrate that HMLC can be an effective means to manage missing or incomplete labels.
  arXiv  Detail & Related papers  (2020-09-11T18:50:23Z)
• BS-Net: learning COVID-19 pneumonia severity on a large Chest X-Ray dataset [6.5800499500032705]
  We design an end-to-end deep learning architecture for predicting, on Chest X-rays images (CXR), a multi-regional score conveying the degree of lung compromise in COVID-19 patients. We exploit a clinical dataset of almost 5,000 CXR annotated images collected in the same hospital. Our solution outperforms single human annotators in rating accuracy and consistency.
  arXiv  Detail & Related papers  (2020-06-08T13:55:58Z)
• Deep Mining External Imperfect Data for Chest X-ray Disease Screening [57.40329813850719]
  We argue that incorporating an external CXR dataset leads to imperfect training data, which raises the challenges. We formulate the multi-label disease classification problem as weighted independent binary tasks according to the categories. Our framework simultaneously models and tackles the domain and label discrepancies, enabling superior knowledge mining ability.
  arXiv  Detail & Related papers  (2020-06-06T06:48:40Z)
• Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Model [71.80319052891817]
  We present a relation-driven semi-supervised framework for medical image classification. It exploits the unlabeled data by encouraging the prediction consistency of given input under perturbations. Our method outperforms many state-of-the-art semi-supervised learning methods on both single-label and multi-label image classification scenarios.
  arXiv  Detail & Related papers  (2020-05-15T06:57:54Z)
• Dynamic Graph Correlation Learning for Disease Diagnosis with Incomplete Labels [66.57101219176275]
  Disease diagnosis on chest X-ray images is a challenging multi-label classification task. We propose a Disease Diagnosis Graph Convolutional Network (DD-GCN) that presents a novel view of investigating the inter-dependency among different diseases. Our method is the first to build a graph over the feature maps with a dynamic adjacency matrix for correlation learning.
  arXiv  Detail & Related papers  (2020-02-26T17:10:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.