Deep learning for understanding multilabel imbalanced Chest X-ray datasets

• URL: http://arxiv.org/abs/2207.14408v1
• Date: Thu, 28 Jul 2022 23:19:53 GMT
• Title: Deep learning for understanding multilabel imbalanced Chest X-ray datasets
• Abstract summary: This paper is a Deep Learning methodology for imbalanced, multilabel chest X-ray datasets. It establishes a baseline for the currently underutilised PadChest dataset and a new eXplainable AI technique based on heatmaps. The results of our system are promising, especially considering the number of labels used.
• Score: 7.885207996427683
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Over the last few years, convolutional neural networks (CNNs) have dominated the field of computer vision thanks to their ability to extract features and their outstanding performance in classification problems, for example in the automatic analysis of X-rays. Unfortunately, these neural networks are considered black-box algorithms, i.e. it is impossible to understand how the algorithm has achieved the final result. To apply these algorithms in different fields and test how the methodology works, we need to use eXplainable AI techniques. Most of the work in the medical field focuses on binary or multiclass classification problems. However, in many real-life situations, such as chest X-rays, radiological signs of different diseases can appear at the same time. This gives rise to what is known as "multilabel classification problems". A disadvantage of these tasks is class imbalance, i.e. different labels do not have the same number of samples. The main contribution of this paper is a Deep Learning methodology for imbalanced, multilabel chest X-ray datasets. It establishes a baseline for the currently underutilised PadChest dataset and a new eXplainable AI technique based on heatmaps. This technique also includes probabilities and inter-model matching. The results of our system are promising, especially considering the number of labels used. Furthermore, the heatmaps match the expected areas, i.e. they mark the areas that an expert would use to make the decision.

Related papers

• Multi-task Explainable Skin Lesion Classification [54.76511683427566]
  We propose a few-shot-based approach for skin lesions that generalizes well with few labelled data. The proposed approach comprises a fusion of a segmentation network that acts as an attention module and classification network.
  arXiv  Detail & Related papers  (2023-10-11T05:49:47Z)
• Long-Tailed Classification of Thorax Diseases on Chest X-Ray: A New Benchmark Study [75.05049024176584]
  We present a benchmark study of the long-tailed learning problem in the specific domain of thorax diseases on chest X-rays. We focus on learning from naturally distributed chest X-ray data, optimizing classification accuracy over not only the common "head" classes, but also the rare yet critical "tail" classes. The benchmark consists of two chest X-ray datasets for 19- and 20-way thorax disease classification, containing classes with as many as 53,000 and as few as 7 labeled training images.
  arXiv  Detail & Related papers  (2022-08-29T04:34:15Z)
• Debiasing Deep Chest X-Ray Classifiers using Intra- and Post-processing Methods [9.152759278163954]
  This work presents two novel intra-processing techniques based on fine-tuning and pruning an already-trained neural network. To the best of our knowledge, this is one of the first efforts studying debiasing methods on chest radiographs.
  arXiv  Detail & Related papers  (2022-07-26T10:18:59Z)
• Multi-Label Generalized Zero Shot Learning for the Classification of Disease in Chest Radiographs [0.7734726150561088]
  We propose a zero shot learning network that can simultaneously predict multiple seen and unseen diseases in chest X-ray images. The network is end-to-end trainable and requires no independent pre-training for the offline feature extractor. Our network outperforms two strong baselines in terms of recall, precision, f1 score, and area under the receiver operating characteristic curve.
  arXiv  Detail & Related papers  (2021-07-14T09:04:20Z)
• Theoretical Insights Into Multiclass Classification: A High-dimensional Asymptotic View [82.80085730891126]
  We provide the first modernally precise analysis of linear multiclass classification. Our analysis reveals that the classification accuracy is highly distribution-dependent. The insights gained may pave the way for a precise understanding of other classification algorithms.
  arXiv  Detail & Related papers  (2020-11-16T05:17:29Z)
• Matching the Clinical Reality: Accurate OCT-Based Diagnosis From Few Labels [2.891413712995642]
  Unlabeled data is often abundant in the clinic, making machine learning methods based on semi-supervised learning a good match for this setting. Recently proposed MixMatch and FixMatch algorithms have demonstrated promising results in extracting useful representations. We find that both algorithms outperform the transfer learning baseline on all fractions of labelled data.
  arXiv  Detail & Related papers  (2020-10-23T11:47:28Z)
• Learning Invariant Feature Representation to Improve Generalization across Chest X-ray Datasets [55.06983249986729]
  We show that a deep learning model performing well when tested on the same dataset as training data starts to perform poorly when it is tested on a dataset from a different source. By employing an adversarial training strategy, we show that a network can be forced to learn a source-invariant representation.
  arXiv  Detail & Related papers  (2020-08-04T07:41:15Z)
• Multi-label Thoracic Disease Image Classification with Cross-Attention Networks [65.37531731899837]
  We propose a novel scheme of Cross-Attention Networks (CAN) for automated thoracic disease classification from chest x-ray images. We also design a new loss function that beyond cross-entropy loss to help cross-attention process and is able to overcome the imbalance between classes and easy-dominated samples within each class.
  arXiv  Detail & Related papers  (2020-07-21T14:37:00Z)
• Analysing Risk of Coronary Heart Disease through Discriminative Neural Networks [18.124078832445967]
  In critical applications like diagnostics, this class imbalance cannot be overlooked. We depict how we can handle this class imbalance through neural networks using a discriminative model and contrastive loss.
  arXiv  Detail & Related papers  (2020-06-17T06:30:00Z)
• 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)

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.