VinDr-SpineXR: A deep learning framework for spinal lesions detection and classification from radiographs

• URL: http://arxiv.org/abs/2106.12930v1
• Date: Thu, 24 Jun 2021 11:45:44 GMT
• Title: VinDr-SpineXR: A deep learning framework for spinal lesions detection and classification from radiographs
• Authors: Hieu T. Nguyen, Hieu H. Pham, Nghia T. Nguyen, Ha Q. Nguyen, Thang Q. Huynh, Minh Dao, Van Vu
• Abstract summary: This work aims at developing and evaluating a deep learning-based framework, named VinDr-SpineXR, for the classification and localization of abnormalities from spine X-rays. We build a large dataset, comprising 10,468 spine X-ray images from 5,000 studies, each of which is manually annotated by an experienced radiologist with bounding boxes around abnormal findings in 13 categories. The VinDr-SpineXR is evaluated on a test set of 2,078 images from 1,000 studies, which is kept separate from the training set.
• Score: 0.812774532310979
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Radiographs are used as the most important imaging tool for identifying spine anomalies in clinical practice. The evaluation of spinal bone lesions, however, is a challenging task for radiologists. This work aims at developing and evaluating a deep learning-based framework, named VinDr-SpineXR, for the classification and localization of abnormalities from spine X-rays. First, we build a large dataset, comprising 10,468 spine X-ray images from 5,000 studies, each of which is manually annotated by an experienced radiologist with bounding boxes around abnormal findings in 13 categories. Using this dataset, we then train a deep learning classifier to determine whether a spine scan is abnormal and a detector to localize 7 crucial findings amongst the total 13. The VinDr-SpineXR is evaluated on a test set of 2,078 images from 1,000 studies, which is kept separate from the training set. It demonstrates an area under the receiver operating characteristic curve (AUROC) of 88.61% (95% CI 87.19%, 90.02%) for the image-level classification task and a mean average precision (mAP@0.5) of 33.56% for the lesion-level localization task. These results serve as a proof of concept and set a baseline for future research in this direction. To encourage advances, the dataset, codes, and trained deep learning models are made publicly available.

Related papers

• 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)
• 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)
• Optimising Chest X-Rays for Image Analysis by Identifying and Removing Confounding Factors [49.005337470305584]
  During the COVID-19 pandemic, the sheer volume of imaging performed in an emergency setting for COVID-19 diagnosis has resulted in a wide variability of clinical CXR acquisitions. The variable quality of clinically-acquired CXRs within publicly available datasets could have a profound effect on algorithm performance. We propose a simple and effective step-wise approach to pre-processing a COVID-19 chest X-ray dataset to remove undesired biases.
  arXiv  Detail & Related papers  (2022-08-22T13:57:04Z)
• An Accurate and Explainable Deep Learning System Improves Interobserver Agreement in the Interpretation of Chest Radiograph [0.33598755777055367]
  The VinDr-CXR can classify a CXR scan into multiple thoracic diseases and localize most types of critical findings on the image. The proposed system significantly improved the agreement between radiologists themselves with an increase of 1.5% in mean Fleiss' Kappa.
  arXiv  Detail & Related papers  (2022-08-06T17:03:49Z)
• 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)
• Sensitivity and Specificity Evaluation of Deep Learning Models for Detection of Pneumoperitoneum on Chest Radiographs [0.8437813529429724]
  State-of-the-art deep learning models (ResNet101, InceptionV3, DenseNet161, and ResNeXt101) were trained on a subset of this dataset. DenseNet161 model was able to accurately classify radiographs from different imaging systems.
  arXiv  Detail & Related papers  (2020-10-17T21:41:53Z)
• COVID-19 Classification of X-ray Images Using Deep Neural Networks [36.99143569437537]
  The purpose of this study is to create and evaluate a machine learning model for diagnosis of COVID-19. A machine learning model was built using a pre-trained deep learning model (ReNet50) and enhanced by data augmentation and lung segmentation. The model was evaluated using accuracy, sensitivity, area under the curve (AUC) of receiver operating characteristic (ROC) curve and of the precision-recall (P-R) curve.
  arXiv  Detail & Related papers  (2020-10-03T13:57:08Z)
• Evaluation of Contemporary Convolutional Neural Network Architectures for Detecting COVID-19 from Chest Radiographs [0.0]
  We train and evaluate three model architectures, proposed for chest radiograph analysis, under varying conditions. We find issues that discount the impressive model performances proposed by contemporary studies on this subject.
  arXiv  Detail & Related papers  (2020-06-30T15:22:39Z)
• 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)
• Y-Net for Chest X-Ray Preprocessing: Simultaneous Classification of Geometry and Segmentation of Annotations [70.0118756144807]
  This work introduces a general pre-processing step for chest x-ray input into machine learning algorithms. A modified Y-Net architecture based on the VGG11 encoder is used to simultaneously learn geometric orientation and segmentation of radiographs. Results were evaluated by expert clinicians, with acceptable geometry in 95.8% and annotation mask in 96.2%, compared to 27.0% and 34.9% respectively in control images.
  arXiv  Detail & Related papers  (2020-05-08T02:16:17Z)
• A neural network model that learns differences in diagnosis strategies among radiologists has an improved area under the curve for aneurysm status classification in magnetic resonance angiography image series [0.0]
  This retrospective study included 3423 time-of-flight brain magnetic resonance angiography image series. The image series were read independently for aneurysm status by one of four board-certified radiologists. The constructed neural networks were trained to classify the aneurysm status of zero to five aneurysm-suspicious areas.
  arXiv  Detail & Related papers  (2020-02-03T19:19:57Z)

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.