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

• URL: http://arxiv.org/abs/2002.01891v1
• Date: Mon, 3 Feb 2020 19:19:57 GMT
• Title: 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
• Authors: Yasuhiko Tachibana, Masataka Nishimori, Naoyuki Kitamura, Kensuke Umehara, Junko Ota, Takayuki Obata, and Tatsuya Higashi
• Abstract summary: 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Purpose: To construct a neural network model that can learn the different diagnosing strategies of radiologists to better classify aneurysm status in magnetic resonance angiography images. Materials and methods: This retrospective study included 3423 time-of-flight brain magnetic resonance angiography image series (subjects: male 1843 [mean age, 50.2 +/- 11.7 years], female 1580 [50.8 +/- 11.3 years]) recorded from November 2017 through January 2019. The image series were read independently for aneurysm status by one of four board-certified radiologists, who were assisted by an established deep learning-based computer-assisted diagnosis (CAD) system. The constructed neural networks were trained to classify the aneurysm status of zero to five aneurysm-suspicious areas suggested by the CAD system for each image series, and any additional aneurysm areas added by the radiologists, and this classification was compared with the judgment of the annotating radiologist. Image series were randomly allocated to training and testing data in an 8:2 ratio. The accuracy of the classification was compared by receiver operating characteristic analysis between the control model that accepted only image data as input and the proposed model that additionally accepted the information of who the annotating radiologist was. The DeLong test was used to compare areas under the curves (P < 0.05 was considered significant). Results: The area under the curve was larger in the proposed model (0.845) than in the control model (0.793), and the difference was significant (P < 0.0001). Conclusion: The proposed model improved classification accuracy by learning the diagnosis strategies of individual annotating radiologists.

Related papers

• Incorporating Anatomical Awareness for Enhanced Generalizability and Progression Prediction in Deep Learning-Based Radiographic Sacroiliitis Detection [0.8248058061511542]
  The aim of this study was to examine whether incorporating anatomical awareness into a deep learning model can improve generalizability and enable prediction of disease progression. The performance of the models was compared using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity.
  arXiv  Detail & Related papers  (2024-05-12T20:02:25Z)
• Beyond Images: An Integrative Multi-modal Approach to Chest X-Ray Report Generation [47.250147322130545]
  Image-to-text radiology report generation aims to automatically produce radiology reports that describe the findings in medical images. Most existing methods focus solely on the image data, disregarding the other patient information accessible to radiologists. We present a novel multi-modal deep neural network framework for generating chest X-rays reports by integrating structured patient data, such as vital signs and symptoms, alongside unstructured clinical notes.
  arXiv  Detail & Related papers  (2023-11-18T14:37:53Z)
• Significantly improving zero-shot X-ray pathology classification via fine-tuning pre-trained image-text encoders [51.14431540035141]
  We propose a new fine-tuning strategy based on sentence sampling and positive-pair loss relaxation for improving the downstream zero-shot pathology classification performance. Our method consistently showed dramatically improved zero-shot pathology classification performance on four different chest X-ray datasets.
  arXiv  Detail & Related papers  (2022-12-14T06:04:18Z)
• TotalSegmentator: robust segmentation of 104 anatomical structures in CT images [48.50994220135258]
  We present a deep learning segmentation model for body CT images. The model can segment 104 anatomical structures relevant for use cases such as organ volumetry, disease characterization, and surgical or radiotherapy planning.
  arXiv  Detail & Related papers  (2022-08-11T15:16:40Z)
• Deep Learning for Classification of Thyroid Nodules on Ultrasound: Validation on an Independent Dataset [7.4674725823899175]
  The purpose is to apply a previously validated deep learning algorithm to a new thyroid ultrasound image dataset. The relative performance difference between the algorithm and the radiologists is not significantly affected by the difference of ultrasound scanner.
  arXiv  Detail & Related papers  (2022-07-27T19:45:41Z)
• Automated SSIM Regression for Detection and Quantification of Motion Artefacts in Brain MR Images [54.739076152240024]
  Motion artefacts in magnetic resonance brain images are a crucial issue. The assessment of MR image quality is fundamental before proceeding with the clinical diagnosis. An automated image quality assessment based on the structural similarity index (SSIM) regression has been proposed here.
  arXiv  Detail & Related papers  (2022-06-14T10:16:54Z)
• 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)
• VinDr-SpineXR: A deep learning framework for spinal lesions detection and classification from radiographs [0.812774532310979]
  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.
  arXiv  Detail & Related papers  (2021-06-24T11:45:44Z)
• Variational Knowledge Distillation for Disease Classification in Chest X-Rays [102.04931207504173]
  We propose itvariational knowledge distillation (VKD), which is a new probabilistic inference framework for disease classification based on X-rays. We demonstrate the effectiveness of our method on three public benchmark datasets with paired X-ray images and EHRs.
  arXiv  Detail & Related papers  (2021-03-19T14:13:56Z)
• 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)

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.