scholarly journals Data valuation for medical imaging using Shapley value and application to a large-scale chest X-ray dataset

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Siyi Tang ◽  
Amirata Ghorbani ◽  
Rikiya Yamashita ◽  
Sameer Rehman ◽  
Jared A. Dunnmon ◽  
...  
Keyword(s):  
Medical Imaging ◽  
Shapley Value ◽  
Measurement Errors ◽  
Large Scale ◽  
Training Data ◽  
Quality Data ◽  
X Ray ◽  
Shapley Values ◽  
Chest X Ray ◽  
Data Valuation

AbstractThe reliability of machine learning models can be compromised when trained on low quality data. Many large-scale medical imaging datasets contain low quality labels extracted from sources such as medical reports. Moreover, images within a dataset may have heterogeneous quality due to artifacts and biases arising from equipment or measurement errors. Therefore, algorithms that can automatically identify low quality data are highly desired. In this study, we used data Shapley, a data valuation metric, to quantify the value of training data to the performance of a pneumonia detection algorithm in a large chest X-ray dataset. We characterized the effectiveness of data Shapley in identifying low quality versus valuable data for pneumonia detection. We found that removing training data with high Shapley values decreased the pneumonia detection performance, whereas removing data with low Shapley values improved the model performance. Furthermore, there were more mislabeled examples in low Shapley value data and more true pneumonia cases in high Shapley value data. Our results suggest that low Shapley value indicates mislabeled or poor quality images, whereas high Shapley value indicates data that are valuable for pneumonia detection. Our method can serve as a framework for using data Shapley to denoise large-scale medical imaging datasets.

scholarly journals Transfer Learning for COVID-19 Pneumonia Detection and Classification in Chest X-ray Images

2020 ◽  
Author(s):  
Iason Katsamenis ◽  
Eftychios Protopapadakis ◽  
Athanasios Voulodimos ◽  
Anastasios Doulamis ◽  
Nikolaos Doulamis
Keyword(s):  
Neural Networks ◽  
Transfer Learning ◽  
Bacterial Pneumonia ◽  
Large Scale ◽  
Training Data ◽  
Classification Models ◽  
Automated Diagnosis ◽  
X Ray ◽  
Learning Framework ◽  
Chest X Ray

We introduce a deep learning framework that can detect COVID-19 pneumonia in thoracic radiographs, as well as differentiate it from bacterial pneumonia infection. Deep classification models, such as convolutional neural networks (CNNs), require large-scale datasets in order to be trained and perform properly. Since the number of X-ray samples related to COVID-19 is limited, transfer learning (TL) appears as the go-to method to alleviate the demand for training data and develop accurate automated diagnosis models. In this context, networks are able to gain knowledge from pretrained networks on large-scale image datasets or alternative data-rich sources (i.e. bacterial and viral pneumonia radiographs). The experimental results indicate that the TL approach outperforms the performance obtained without TL, for the COVID-19 classification task in chest X-ray images.

scholarly journals Analyse Du Rebut Des Cliches Radiographiques Dans Le Service De Radiologie Et Imagerie Médicale Du Chu Campus De Lomé Au Togo

2017 ◽  
Vol 13 (33) ◽  
pp. 244
Author(s):  
P. Gbande ◽  
L. Sonhaye ◽  
K. Adambounou ◽  
K. Lambon ◽  
B. N’timon ◽  
...  
Keyword(s):  
Medical Imaging ◽  
Prospective Study ◽  
Lower Limb ◽  
University Hospital ◽  
X Rays ◽  
Financial Loss ◽  
X Ray ◽  
Chest X Ray ◽  
Almost All

Purpose: To analyze the waste factors of rejected X-rays films. Methodology: Descriptive and analytical prospective study from 1 January to 30 June 2017 carried out in the department of radiology and medical imaging of the Campus University Hospital of Lomé in Togo. Results: 4912 patients had received 5630 radiographic incidences, including 3288 (58.4%) on the analogy and 2342 (41.5%) on the digital. The reject rate was 12.5%. The vast majority of the X-rays films, 682 (96.9%) were rejected by the radiographers themselves just after development. The resumption frequency ranged from one repeat (550 X-rays films, or 78%) to 4 repeats (8 X-rays films, or 1%). Almost all of the rejected films, 702 (99.7%) came from the analogical room. Chest X-ray was the incidence with more rejection in 33.9% followed by pelvic and lower limb incidences in 21% of cases. More than 2/3 of the rejected films, 473 (67.2%), came from the students' act. The causes of the rejection were mainly centering (25.5%), underexposure (20.17%) and overexposure (12.93). The financial loss caused by the scrap of X-rays films amounted to about 418800F CFA or 638.5 €. Conclusion: Strengthening communication between radiographers and radiologists is necessary to avoid unnecessary repeats of patient’s radiographs.

scholarly journals Novel Method for Detection of Sars-COV2 Using Chest X-Ray Images

2021 ◽  
pp. 145-149
Author(s):  
Mrs Tejaswini ML ◽  
Ashwni H ◽  
Chandana N ◽  
Harshitha BR ◽  
Nagashree HN
Keyword(s):  
Public Health ◽  
Health Sector ◽  
Cost Effective ◽  
Fast Response ◽  
Training Data ◽  
X Ray ◽  
Public Areas ◽  
Normal Chest ◽  
Chest X Ray ◽  
Radiograph Image

A coronavirus have a great impact on a public health globally. Real time PCR s used for pathological testing but that result in false test result this impact made to exploration of alternate method for testing [1]. The detection of coronavirus 2 using chest X-ray image is anlifesaving property. By using chest X-ray coronavirus can identified are cost effective and its available on every public health sector rural clinic hospital. Deep learning –based chest radiograph classification (DL-CRC) frame are used to distinguish the COVID-19 cases and normal cases will high accuracy. The pre-trained image database used for large training sets to have pre- trained weights .The training data consisting covid chest X-ray image and normal chest X-ray image and fed into customized convolution neural network (CNN) model in DL-CRC wear masks in public areas is a major protection for people .The classification implies that it can efficiently detection COVID-19 from radiograph image for provide a reliable and fast response of COVID-19 infection in the lung.

scholarly journals Training deep learning algorithms with weakly labeled pneumonia chest X-ray data for COVID-19 detection

2020 ◽  
Author(s):  
Sivaramakrishnan Rajaraman ◽  
Sameer Antani
Keyword(s):  
Data Augmentation ◽  
Feature Space ◽  
Distinct Pattern ◽  
Viral Pneumonia ◽  
Training Data ◽  
Polymerase Chain Reaction Test ◽  
X Ray ◽  
Chest X Ray ◽  
Baseline Test ◽  
Chain Reaction Test

AbstractThe novel Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has caused a pandemic resulting in over 2.7 million infected individuals and over 190,000 deaths and growing. Respiratory disorders in COVID-19 caused by the virus commonly present as viral pneumonia-like opacities in chest X-ray images which are used as an adjunct to the reverse transcription-polymerase chain reaction test for confirmation and evaluating disease progression. The surge places high demand on medical services including radiology expertise. However, there is a dearth of sufficient training data for developing image-based automated decision support tools to alleviate radiological burden. We address this insufficiency by expanding training data distribution through use of weakly-labeled images pooled from publicly available CXR collections showing pneumonia-related opacities. We use the images in a stage-wise, strategic approach and train convolutional neural network-based algorithms to detect COVID-19 infections in CXRs. It is observed that weakly-labeled data augmentation improves performance with the baseline test data compared to non-augmented training by expanding the learned feature space to encompass variability in the unseen test distribution to enhance inter-class discrimination, reduce intra-class similarity and generalization error. Augmentation with COVID-19 CXRs from individual collections significantly improves performance compared to baseline non-augmented training and weakly-labeled augmentation toward detecting COVID-19 like viral pneumonia in the publicly available COVID-19 CXR collections. This underscores the fact that COVID-19 CXRs have a distinct pattern and hence distribution, unlike non-COVID-19 viral pneumonia and other infectious agents.

Deep neural network ensemble for pneumonia localization from a large-scale chest x-ray database

2019 ◽  
Vol 78 ◽  
pp. 388-399 ◽  
Author(s):  
Ilyas Sirazitdinov ◽  
Maksym Kholiavchenko ◽  
Tamerlan Mustafaev ◽  
Yuan Yixuan ◽  
Ramil Kuleev ◽  
...  
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Deep Neural Network ◽  
Neural Network Ensemble ◽  
X Ray ◽  
Chest X Ray

scholarly journals Explainable DCNN based chest X-ray image analysis and classification for COVID-19 pneumonia detection

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jie Hou ◽  
Terry Gao
Keyword(s):  
Large Scale ◽  
Rapid Screening ◽  
X Rays ◽  
X Ray ◽  
Disease Mechanisms ◽  
Average Accuracy ◽  
Speed Up ◽  
Chest X Ray ◽  
Detection And Diagnosis ◽  
New Diagnosis

AbstractTo speed up the discovery of COVID-19 disease mechanisms by X-ray images, this research developed a new diagnosis platform using a deep convolutional neural network (DCNN) that is able to assist radiologists with diagnosis by distinguishing COVID-19 pneumonia from non-COVID-19 pneumonia in patients based on chest X-ray classification and analysis. Such a tool can save time in interpreting chest X-rays and increase the accuracy and thereby enhance our medical capacity for the detection and diagnosis of COVID-19. The explainable method is also used in the DCNN to select instances of the X-ray dataset images to explain the behavior of training-learning models to achieve higher prediction accuracy. The average accuracy of our method is above 96%, which can replace manual reading and has the potential to be applied to large-scale rapid screening of COVID-9 for widely use cases.

scholarly journals Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations

2021 ◽  
Author(s):  
Laleh Seyyed-Kalantari ◽  
Haoran Zhang ◽  
Matthew B. A. McDermott ◽  
Irene Y. Chen ◽  
Marzyeh Ghassemi
Keyword(s):  
Artificial Intelligence ◽  
Medical Imaging ◽  
Low Socioeconomic Status ◽  
Disease Diagnosis ◽  
Low Socioeconomic ◽  
X Ray ◽  
Female Patients ◽  
Pathology Classification ◽  
Black Patients ◽  
Chest X Ray

AbstractArtificial intelligence (AI) systems have increasingly achieved expert-level performance in medical imaging applications. However, there is growing concern that such AI systems may reflect and amplify human bias, and reduce the quality of their performance in historically under-served populations such as female patients, Black patients, or patients of low socioeconomic status. Such biases are especially troubling in the context of underdiagnosis, whereby the AI algorithm would inaccurately label an individual with a disease as healthy, potentially delaying access to care. Here, we examine algorithmic underdiagnosis in chest X-ray pathology classification across three large chest X-ray datasets, as well as one multi-source dataset. We find that classifiers produced using state-of-the-art computer vision techniques consistently and selectively underdiagnosed under-served patient populations and that the underdiagnosis rate was higher for intersectional under-served subpopulations, for example, Hispanic female patients. Deployment of AI systems using medical imaging for disease diagnosis with such biases risks exacerbation of existing care biases and can potentially lead to unequal access to medical treatment, thereby raising ethical concerns for the use of these models in the clinic.

scholarly journals Tinjauan Literatur Sistematik tentang Deteksi Covid-19 menggunakan Convolutional Neural Networks

2021 ◽  
Vol 8 (1) ◽  
pp. 9
Author(s):  
Buyut Khoirul Umri ◽  
Ema Utami ◽  
Mei P Kurniawan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Literature Review ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Large Scale ◽  
Image Data ◽  
X Rays ◽  
X Ray ◽  
Chest X Ray

Covid-19 menyerang sel-sel epitel yang melapisi saluran pernapasan sehingga dalam kasus ini dapat memanfaatkan gambar x-ray dada untuk menganalisis kesehatan paru-paru pada pasien. Menggunakan x-ray dalam bidang medis merupakan metode yang lebih cepat, lebih mudah dan tidak berbahaya yang dapat dimanfaatkan pada banyak hal. Salah satu metode yang paling sering digunakan dalam klasifikasi gambar adalah convolutional neural networks (CNN). CNN merupahan jenis neural network yang sering digunakan dalam data gambar dan sering digunakan dalam mendeteksi dan mengenali object pada sebuah gambar. Model arsitektur pada metode CNN juga dapat dikembangkan dengan transfer learning yang merupakan proses menggunakan kembali model pre-trained yang dilatih pada dataset besar, biasanya pada tugas klasifikasi gambar berskala besar. Tinjauan literature review ini digunakan untuk menganalisis penggunaan transfer learning pada CNN sebagai metode yang dapat digunakan untuk mendeteksi covid-19 pada gambar x-ray dada. Hasil sistematis review menunjukkan bahwa algoritma CNN dapat digunakan dengan akruasi yang baik dalam mendeteksi covid-19 pada gambar x-ray dada dan dengan pengembangan model transfer learning mampu mendapatkan performa yang maksimal dengan dataset yang besar maupun kecil.Kata Kunci—CNN, transfer learning, deteksi, covid-19Covid-19 attacks the epithelial cells lining the respiratory tract so that in this case it can utilize chest x-ray images to analyze the health of the lungs in patients. Using x-rays in the medical field is a faster, easier and harmless method that can be utilized in many ways. One of the most frequently used methods in image classification is convolutional neural networks (CNN). CNN is a type of neural network that is often used in image data and is often used in detecting and recognizing objects in an image. The architectural model in the CNN method can also be developed with transfer learning which is the process of reusing pre-trained models that are trained on large datasets, usually on the task of classifying large-scale images. This literature review review is used to analyze the use of transfer learning on CNN as a method that can be used to detect covid-19 on chest x-ray images. The systematic review results show that the CNN algorithm can be used with good accuracy in detecting covid-19 on chest x-ray images and by developing transfer learning models able to get maximum performance with large and small datasets.Keywords—CNN, transfer learning, detection, covid-19

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