Unsupervised Detection of Lung Nodules in Chest Radiography Using Generative Adversarial Networks

• URL: http://arxiv.org/abs/2108.02233v1
• Date: Wed, 4 Aug 2021 18:24:18 GMT
• Title: Unsupervised Detection of Lung Nodules in Chest Radiography Using Generative Adversarial Networks
• Authors: Nitish Bhatt, David Ramon Prados, Nedim Hodzic, Christos Karanassios, and H.R. Tizhoosh
• Abstract summary: We propose and evaluate P-AnoGAN, an unsupervised anomaly detection approach for lung nodules in radiographs. P-AnoGAN modifies the fast anomaly detection generative adversarial network (f-AnoGAN) by utilizing a progressive GAN and a convolutional encoder-decoder-encoder pipeline. Our model robustly identifies patches containing lung nodules in external validation and test data with ROC-AUC of 91.17% and 87.89%, respectively.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Lung nodules are commonly missed in chest radiographs. We propose and evaluate P-AnoGAN, an unsupervised anomaly detection approach for lung nodules in radiographs. P-AnoGAN modifies the fast anomaly detection generative adversarial network (f-AnoGAN) by utilizing a progressive GAN and a convolutional encoder-decoder-encoder pipeline. Model training uses only unlabelled healthy lung patches extracted from the Indiana University Chest X-Ray Collection. External validation and testing are performed using healthy and unhealthy patches extracted from the ChestX-ray14 and Japanese Society for Radiological Technology datasets, respectively. Our model robustly identifies patches containing lung nodules in external validation and test data with ROC-AUC of 91.17% and 87.89%, respectively. These results show unsupervised methods may be useful in challenging tasks such as lung nodule detection in radiographs.

Related papers

• Nodule detection and generation on chest X-rays: NODE21 Challenge [33.99989671569125]
  Deep learning methods can yield high-performance levels in the detection of lung nodules in chest X-rays. We organized a public research challenge, NODE21, aimed at the detection and generation of lung nodules in chest X-rays. This paper summarizes the results of the NODE21 challenge and performs additional experiments to examine the impact of the synthetically generated training images on the detection algorithm performance.
  arXiv  Detail & Related papers  (2024-01-04T10:54:05Z)
• SGDA: Towards 3D Universal Pulmonary Nodule Detection via Slice Grouped Domain Attention [47.44114201293201]
  Lung cancer is the leading cause of cancer death worldwide. Current pulmonary nodule detection methods are usually domain-specific. We propose a slice grouped domain attention (SGDA) module to enhance the generalization capability of the pulmonary nodule detection networks.
  arXiv  Detail & Related papers  (2023-03-07T03:17:49Z)
• Image Synthesis with Disentangled Attributes for Chest X-Ray Nodule Augmentation and Detection [52.93342510469636]
  Lung nodule detection in chest X-ray (CXR) images is common to early screening of lung cancers. Deep-learning-based Computer-Assisted Diagnosis (CAD) systems can support radiologists for nodule screening in CXR. To alleviate the limited availability of such datasets, lung nodule synthesis methods are proposed for the sake of data augmentation.
  arXiv  Detail & Related papers  (2022-07-19T16:38:48Z)
• Multi-Task Lung Nodule Detection in Chest Radiographs with a Dual Head Network [0.7614628596146599]
  Lung nodules can be an alarming precursor to potential lung cancer. Missed nodule detections during chest radiograph analysis remains a common challenge. We present a multi-task lung nodule detection algorithm for chest radiograph analysis.
  arXiv  Detail & Related papers  (2022-07-07T02:29:34Z)
• X-ray Dissectography Improves Lung Nodule Detection [14.672019886848755]
  "X-ray dissectography" is applied to dissect lungs digitally from a few radiographic projections. A collaborative detection network is designed to localize lung nodules in 2D dissected projections and 3D physical space.
  arXiv  Detail & Related papers  (2022-03-24T15:18:57Z)
• Debiasing pipeline improves deep learning model generalization for X-ray based lung nodule detection [11.228544549618068]
  Lung cancer is the leading cause of cancer death worldwide and a good prognosis depends on early diagnosis. We show that an image pre-processing pipeline that homogenizes and debiases chest X-ray images can improve both internal classification and external generalization. An evolutionary pruning mechanism is used to train a nodule detection deep learning model on the most informative images from a publicly available lung nodule X-ray dataset.
  arXiv  Detail & Related papers  (2022-01-24T10:08:07Z)
• CoRSAI: A System for Robust Interpretation of CT Scans of COVID-19 Patients Using Deep Learning [133.87426554801252]
  We adopted an approach based on using an ensemble of deep convolutionalneural networks for segmentation of lung CT scans. Using our models we are able to segment the lesions, evaluatepatients dynamics, estimate relative volume of lungs affected by lesions and evaluate the lung damage stage.
  arXiv  Detail & Related papers  (2021-05-25T12:06:55Z)
• Self-supervised deep convolutional neural network for chest X-ray classification [0.0]
  We propose a self-supervised deep neural network that is pretrained on an unlabeled chest X-ray dataset. The results obtained on four public datasets show that our approach yields competitive results without requiring large amounts of labeled training data.
  arXiv  Detail & Related papers  (2021-03-04T14:28:37Z)
• 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)
• Deep Learning for Automatic Pneumonia Detection [72.55423549641714]
  Pneumonia is the leading cause of death among young children and one of the top mortality causes worldwide. Computer-aided diagnosis systems showed the potential for improving diagnostic accuracy. We develop the computational approach for pneumonia regions detection based on single-shot detectors, squeeze-and-excitation deep convolution neural networks, augmentations and multi-task learning.
  arXiv  Detail & Related papers  (2020-05-28T10:54:34Z)
• Predicting COVID-19 Pneumonia Severity on Chest X-ray with Deep Learning [57.00601760750389]
  We present a severity score prediction model for COVID-19 pneumonia for frontal chest X-ray images. Such a tool can gauge severity of COVID-19 lung infections that can be used for escalation or de-escalation of care.
  arXiv  Detail & Related papers  (2020-05-24T23:13:16Z)

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.