Weighing Features of Lung and Heart Regions for Thoracic Disease Classification

• URL: http://arxiv.org/abs/2105.12430v1
• Date: Wed, 26 May 2021 09:37:39 GMT
• Title: Weighing Features of Lung and Heart Regions for Thoracic Disease Classification
• Authors: Jiansheng Fang, Yanwu Xu, Yitian Zhao, Yuguang Yan, Junling Liu and Jiang Liu
• Abstract summary: We propose a novel deep learning framework to explore discriminative information from lung and heart regions. By zeroing features of non-lung and heart regions in attention maps, we can effectively exploit their disease-specific cues in lung and heart regions.
• Score: 15.128119121297507
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Chest X-rays are the most commonly available and affordable radiological examination for screening thoracic diseases. According to the domain knowledge of screening chest X-rays, the pathological information usually lay on the lung and heart regions. However, it is costly to acquire region-level annotation in practice, and model training mainly relies on image-level class labels in a weakly supervised manner, which is highly challenging for computer-aided chest X-ray screening. To address this issue, some methods have been proposed recently to identify local regions containing pathological information, which is vital for thoracic disease classification. Inspired by this, we propose a novel deep learning framework to explore discriminative information from lung and heart regions. We design a feature extractor equipped with a multi-scale attention module to learn global attention maps from global images. To exploit disease-specific cues effectively, we locate lung and heart regions containing pathological information by a well-trained pixel-wise segmentation model to generate binarization masks. By introducing element-wise logical AND operator on the learned global attention maps and the binarization masks, we obtain local attention maps in which pixels are $1$ for lung and heart region and $0$ for other regions. By zeroing features of non-lung and heart regions in attention maps, we can effectively exploit their disease-specific cues in lung and heart regions. Compared to existing methods fusing global and local features, we adopt feature weighting to avoid weakening visual cues unique to lung and heart regions. Evaluated by the benchmark split on the publicly available chest X-ray14 dataset, the comprehensive experiments show that our method achieves superior performance compared to the state-of-the-art methods.

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)
• Generative Residual Attention Network for Disease Detection [51.60842580044539]
  We present a novel approach for disease generation in X-rays using a conditional generative adversarial learning. We generate a corresponding radiology image in a target domain while preserving the identity of the patient. We then use the generated X-ray image in the target domain to augment our training to improve the detection performance.
  arXiv  Detail & Related papers  (2021-10-25T14:15:57Z)
• AnaXNet: Anatomy Aware Multi-label Finding Classification in Chest X-ray [9.087789790647786]
  We propose a novel multi-label chest X-ray classification model that accurately classifies the image finding and also localizes the findings to their correct anatomical regions. The latter utilizes graph convolutional networks, which enable our model to learn not only the label dependency but also the relationship between the anatomical regions in the chest X-ray.
  arXiv  Detail & Related papers  (2021-05-20T17:58:02Z)
• Global Guidance Network for Breast Lesion Segmentation in Ultrasound Images [84.03487786163781]
  We develop a deep convolutional neural network equipped with a global guidance block (GGB) and breast lesion boundary detection modules. Our network outperforms other medical image segmentation methods and the recent semantic segmentation methods on breast ultrasound lesion segmentation.
  arXiv  Detail & Related papers  (2021-04-05T13:15:22Z)
• Weakly Supervised Thoracic Disease Localization via Disease Masks [29.065791290544983]
  weakly supervised localization methods have been proposed that use only image-level annotation. We propose a spatial attention method using disease masks that describe the areas where diseases mainly occur. We show that the proposed method results in superior localization performances compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-01-25T06:52:57Z)
• Contralaterally Enhanced Networks for Thoracic Disease Detection [120.60868136876599]
  There exist many similar structures in the left and right parts of the chest, such as ribs, lung fields and bronchial tubes. This kind of similarities can be used to identify diseases in chest X-rays, according to the experience of broad-certificated radiologists. We propose a deep end-to-end module to exploit the contralateral context information for enhancing feature representations of disease proposals.
  arXiv  Detail & Related papers  (2020-10-09T10:15:26Z)
• Explainable Disease Classification via weakly-supervised segmentation [4.154485485415009]
  Deep learning approaches to Computer Aided Diagnosis (CAD) typically pose the problem as an image classification (Normal or Abnormal) problem. This paper examines this problem and proposes an approach which mimics the clinical practice of looking for evidence prior to diagnosis. The proposed solution is then adapted to Breast Cancer detection from mammographic images.
  arXiv  Detail & Related papers  (2020-08-24T09:00:30Z)
• 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)
• Thoracic Disease Identification and Localization using Distance Learning and Region Verification [15.851040584898463]
  We propose an alternative approach that learns discriminative features among triplets of images and cyclically trains on region features to verify whether attentive regions contain information indicative of a disease. Our model can achieve state-of-the-art classification performance on the challenging ChestX-ray14 dataset, and our ablation studies indicate that both distance learning and region verification contribute to overall classification performance.
  arXiv  Detail & Related papers  (2020-06-07T16:56:50Z)

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.