A New Window Loss Function for Bone Fracture Detection and Localization in X-ray Images with Point-based Annotation

• URL: http://arxiv.org/abs/2012.04066v2
• Date: Mon, 4 Jan 2021 15:55:50 GMT
• Title: A New Window Loss Function for Bone Fracture Detection and Localization in X-ray Images with Point-based Annotation
• Authors: Xinyu Zhang, Yirui Wang, Chi-Tung Cheng, Le Lu, Adam P. Harrison, Jing Xiao, Chien-Hung Liao, Shun Miao
• Abstract summary: We propose a new bone fracture detection method for X-ray images based on a labor effective and flexible annotation scheme. Our method employs a simple, intuitive, and informative point-based annotation protocol to mark localized information. Our method has been extensively evaluated on 4410 pelvic X-ray images of unique patients.
• Score: 21.004545631297855
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Object detection methods are widely adopted for computer-aided diagnosis using medical images. Anomalous findings are usually treated as objects that are described by bounding boxes. Yet, many pathological findings, e.g., bone fractures, cannot be clearly defined by bounding boxes, owing to considerable instance, shape and boundary ambiguities. This makes bounding box annotations, and their associated losses, highly ill-suited. In this work, we propose a new bone fracture detection method for X-ray images, based on a labor effective and flexible annotation scheme suitable for abnormal findings with no clear object-level spatial extents or boundaries. Our method employs a simple, intuitive, and informative point-based annotation protocol to mark localized pathology information. To address the uncertainty in the fracture scales annotated via point(s), we convert the annotations into pixel-wise supervision that uses lower and upper bounds with positive, negative, and uncertain regions. A novel Window Loss is subsequently proposed to only penalize the predictions outside of the uncertain regions. Our method has been extensively evaluated on 4410 pelvic X-ray images of unique patients. Experiments demonstrate that our method outperforms previous state-of-the-art image classification and object detection baselines by healthy margins, with an AUROC of 0.983 and FROC score of 89.6%.

Related papers

• AnoDODE: Anomaly Detection with Diffusion ODE [0.0]
  Anomaly detection is the process of identifying atypical data samples that significantly deviate from the majority of the dataset. We propose a new anomaly detection method based on diffusion ODEs by estimating the density of features extracted from medical images. Our proposed method not only identifie anomalies but also provides interpretability at both the image and pixel levels.
  arXiv  Detail & Related papers  (2023-10-10T08:44:47Z)
• Anatomy-Driven Pathology Detection on Chest X-rays [17.670821896026137]
  We propose anatomy-driven pathology detection (ADPD), which uses easy-to-annotate bounding boxes of anatomical regions as proxies for pathologies. We study two training approaches: supervised training using anatomy-level pathology labels and multiple instance learning (MIL) with image-level pathology labels. Our results show that our anatomy-level training approach outperforms weakly supervised methods and fully supervised detection with limited training samples.
  arXiv  Detail & Related papers  (2023-09-05T20:58:15Z)
• DeepLOC: Deep Learning-based Bone Pathology Localization and Classification in Wrist X-ray Images [1.45543311565555]
  This paper presents a novel approach for bone pathology localization and classification in wrist X-ray images. The proposed methodology addresses two critical challenges in wrist X-ray analysis: accurate localization of bone pathologies and precise classification of abnormalities.
  arXiv  Detail & Related papers  (2023-08-24T12:06:10Z)
• Employing similarity to highlight differences: On the impact of anatomical assumptions in chest X-ray registration methods [2.080328156648695]
  We develop an anatomically penalized convolutional multi-stage solution on the National Institutes of Health (NIH) data set. Our method proves to be a natural way to limit the folding percentage of the warp field to 1/6 of the state of the art. We statistically evaluate the benefits of our method and highlight the limits of currently used metrics for registration of chest X-rays.
  arXiv  Detail & Related papers  (2023-01-23T09:42:49Z)
• Significantly improving zero-shot X-ray pathology classification via fine-tuning pre-trained image-text encoders [50.689585476660554]
  We propose a new fine-tuning strategy that includes positive-pair loss relaxation and random sentence sampling. Our approach consistently improves overall zero-shot pathology classification across four chest X-ray datasets and three pre-trained models.
  arXiv  Detail & Related papers  (2022-12-14T06:04:18Z)
• SQUID: Deep Feature In-Painting for Unsupervised Anomaly Detection [76.01333073259677]
  We propose the use of Space-aware Memory Queues for In-painting and Detecting anomalies from radiography images (abbreviated as SQUID) We show that SQUID can taxonomize the ingrained anatomical structures into recurrent patterns; and in the inference, it can identify anomalies (unseen/modified patterns) in the image.
  arXiv  Detail & Related papers  (2021-11-26T13:47:34Z)
• 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)
• Cross-Modal Contrastive Learning for Abnormality Classification and Localization in Chest X-rays with Radiomics using a Feedback Loop [63.81818077092879]
  We propose an end-to-end semi-supervised cross-modal contrastive learning framework for medical images. We first apply an image encoder to classify the chest X-rays and to generate the image features. The radiomic features are then passed through another dedicated encoder to act as the positive sample for the image features generated from the same chest X-ray.
  arXiv  Detail & Related papers  (2021-04-11T09:16:29Z)
• 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)
• Unsupervised Anomaly Detection for X-Ray Images [4.353258086186526]
  We investigate how unsupervised methods trained on images without anomalies can be used to assist doctors in evaluating X-ray images of hands. We adopt state-of-the-art approaches for unsupervised learning to detect anomalies and show how the outputs of these methods can be explained. We provide an extensive evaluation of different approaches and demonstrate empirically that even without labels it is possible to achieve satisfying results on a real-world dataset of X-ray images of hands.
  arXiv  Detail & Related papers  (2020-01-29T15:14:56Z)
• Vertebra-Focused Landmark Detection for Scoliosis Assessment [54.24477530836629]
  We propose a novel vertebra-focused landmark detection method. Our model first localizes the vertebra centers, based on which it then traces the four corner landmarks of the vertebra through the learned corner offset. Results demonstrate the merits of our method in both Cobb angle measurement and landmark detection on low-contrast and ambiguous X-ray images.
  arXiv  Detail & Related papers  (2020-01-09T19:17:41Z)

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.