medical image dataset for deep learning

High quality imaging improves medical decision making and can reduce unnecessary medical procedures. This is called tumor or neoplasm. With the advancement and increase in the use of medical imaging, the global market for these manufactured devices for medical imaging is estimated to generate around $48.6 billion by 2025 which was estimated to be $34 billion in 2018(click here). Converting the tuple of labels to numpy array and reshaping them to shape of (n,1) where n being number of samples. At a time where many first-world countries are facing an aging and declining population crisis, machine learning could help us provide better care for the elderly. These images help in assessment of the presence or absence of disease, damage or foreign object. Despite the new performance highs, the recent advanced segmentation models still require large, representative, and high quality annotated datasets. Check out the latest blog articles, webinars, insights, and other resources on Machine Learning, Deep Learning on Nanonets blog.. Doctors use it for the organ study and suggest required treatment schedules and also keep the visual data in their library for future reference in other medical cases too. It provides less anatomical detail relative to CT or MRI scans. The images are annotated with age, modality, and contrast tags. Diabetes is the major cause of blindness, kidney failure, heart attacks, stroke and lower limb amputation. Genome in a Bottle: Dataset includes several reference genomes to enable translation of whole human genome sequencing to clinical practice. It is capable of capturing moving objects in real time. We will review literature about how machine learning is being applied in different spheres of medical imaging and in the end implement a binary classifier to diagnose diabetic retinopathy. The amount of radiation increases with increase in temperature. According to 2018 reports by World Health Organisation(WHO), in 2018, an estimated 228 million cases of malaria occurred worldwide out of which there were an estimated 405,000 deaths from malaria globally. Lionbridge is a registered trademark of Lionbridge Technologies, Inc. Sign up to our newsletter for fresh developments from the world of training data. In 2018, they accounted for 67% (272,000) of all malaria deaths worldwide. The training epochs shown below is the part where my model was able to reach the validation loss minima. Thermographic cameras are quite expensive. Smear microscopy and fluroscent auramine-rhodamin stain or Ziehl-Neelsen stain are standard methods for Tuberculosis diagnosis. As you can see total 1000 training images are only used owing the RAM constraints as well as to create a balanced dataset for training. On the other hand, malignant tumor is extremely harmful spreading to other body parts. The data has been downloaded and segregated using the trainLabels.csv. Interpretation of medical images is quite limited to specific experts owing to its complexity, variety of parameters and most important core knowledge of the subject. Moreover, traditional machine learning can’t comprehend the complexity of such healthcare oriented problem statements owing to the complexity and importance of the subject. Data privacy is both sociological as well as a technical issue, which needs to be addressed from both angles. Plotting of the metrics using matplotlib library has been done in the function plot_metric as shown below. All of these are interconnected, and a shortfall in any of these may lead to subsequent failure … Genus plasmodium parasite are the main cause of malaria and microscopial imaging is the standard method for parasite detection in blood smear samples. Main risks involved with this procedure are infection, over-sedation, perforation, tear lining and bleeding. Benign tumor is not that dangerous and stick to one part of the body and do not spread to other parts. However, the radiation dosage ar small still there’s a potential risk. Current imaging technologies play vital role in diagnosing these disorders concerned with the gastrointestinal tract which include endoscopy, enteroscopy, wireless capsule endoscopy, tomography and MRI. Patients are the end users of treatments received owing the conclusion derived from the images captured. Considering as per the GPU memory allocated for the task we went with the batch size of 8. Image read and resizing to 512 x 512 x 3. Therefore, we are in an age where there has been rapid growth in medical image acquisition as well as running challenging and interesting analysis on them. These datasets are used for machine-learning research and have been cited in peer-reviewed academic journals. Owing to the advancements in the field today medical imaging has the ability to achieve information of human body for many useful clinical applications. Organisations incorporating the medical imaging devices include freestanding radiology and pathology facilities as well as clinics and hospitals. Here, in this section we will create a binary classifier to detect diabetic retinopathy symptoms from the retinal fundus images. Polyps, cancer or diverticulitis cause bleeding from large intestine. Summary of the above devised model can be seen below with output shape from each component layer of the model. Various methods of radiological imaging have generated good amount of data but we are still short of valuable useful data at the disposal to be incorporated by deep learning model. The image dataset contains multiple chest x-rays of a single person. Thermography : Thermographic cameras detect long infrared radiations emitted by the body which create thermal images based on the radiations received. There are a variety of image processing libraries, however OpenCV(open computer vision) has become mainstream due to its large community support and availability in C++, java and python. Want to apply Object Detection in your projects? ... 2.Dataset Design 3.Human-AI Interaction. Therefore, we take the No DR data as no symptom class label and Severe as well as Proliferative DR as the as symptom class label. A study done by Harvard researchers concluded that $385 spent on medical imaging saves approximately $3000 i.e. Ultrasound : Ultrasound uses high frequency broadband MH range sound waves that are reflected by tissue to varying degrees to produce sort of 3D images. Some of the major challenges are as follows: The first and the major prerequisite to use deep learning is massive amount of training dataset as the quality and evaluation of deep learning based classifier relies heavily on quality and amount of the data. SPECT is used for any gamma imaging study which is helpful in treatment specially for tumors, leukocytes, thyroids and bones. The performance on deep learning is significantly affected by volume of training data. However, the usefulness and potential impact of such a system can be completely … Sharing of sensitive data with limited disclosure is a real challenge. Therefore, patients are tested before if their body reacts affirmatively to the radiation used for medical imaging and making sure least possible amount of radiation is used for the process. July 23, 2018 - The National Institutes of Health (NIH) Clinical Center has released a dataset of more than 32,000 medical images to help enhance the accuracy of lesion detection. Further data segregation into two classes namely symptoms and nosymptoms, we read the segregated dataset. Ulcers cause bleeding in the upper gastrointestinal tract. Contribute to sfikas/medical-imaging-datasets development by creating an account on GitHub. AI and Machine Learning in medical imaging is playing a vital role in analysis and diagnosis of various critical diseases with best level of accuracy.Artificial intelligence in medical diagnosis is trained with annotated images like X-Rays, CT Scan, Ultrasound and MRIs reports available in digital formats. Healthcare industry is a high priority sector where majority of the interpretations of medical data are done by medical experts. Green channel selection resulting the tensor to be of shape 512 x 512 x 1. The uphill task being the manual identification of the coronary artery calcium (CAC) scoring in cardiac CT scans which incorporates a good amount of effort. All these images are manually annotated by an expert slide reader at the Mahidol-Oxford Tropical Medicine Research Unit. The organs included are oesophagus, stomach, duodendum, large intestine(colon) and small intestine(small bowel). Medical image classification plays an essential role in clinical treatment and teaching tasks. Therefore, making it to be a time consuming task for epidemiological studies. Computer vision researchers along with doctors can label the image dataset as the severity of the medical condition and type of condition post which the using traditional image processing or modern deep learning based approaches underlying patterns can be captured have a high potential to speed-up the inference process from medical images. Big Cities Health Inventory Data Platform: Health data from 26 cities, for 34 health indicators, across 6 demographic indicators. There are two types of tumor : Benign (non-cancerous) and Malignant (cancerous). the use of deep learning in MR reconstructed images, such as medical image segmentation, super-resolution, medical image synthesis. Best we had till date, was traditional machine learning applications in computer vision which relied heavily on features crafted by medical experts who are the subject matter people of the concerned field. Rei writes content for Lionbridge’s website, blog articles, and social media. MRI is widely used in hospitals and seen as a better choice than a CT scan since MRI helps in medical diagnosis without exposing body to radiation. As mentioned in the above section about different medical imaging techniques, the advancement of image acquisition devices have reduced the challenge of data collection with time. These feature extraction improve with better data and supervision so much that they can help diagnose a physician efficiently. We have discussed the important ones above but there are many more medical imaging techniques helping and providing solutions during various medical cases. Therefore, early detection via effective medical imaging has empowered both the doctors with the opportunity to diagnose ailments early and the patients with the opportunity to fight to live longer. Malaria detection is highly crucial and important. You will also need numpy and matplotlib to vi… Differential privacy approaches can be undertaken which restricts the data to organisation on requirement basis. Deep Learning for Medical Imaging: COVID-19 Detection. Medical Data for Machine Learning. Doctors perform medical imaging to determine the status of the organ and what treatments would be required for the recovery. How to (quickly) build a deep learning image dataset. These earlier machine learning algorithms of Logistic Regression, Support Vector Machines(SVMs), K-Nearest Neighbours(KNNs), Decision Trees etc. Nuclear Medicine Imaging : This type of medical imaging is done by taking radio-pharmaceuticals internally. GEO Datasets: This database stores curated gene expression datasets, as well as original series and platform records in the gene expression omnibus (GEO) repository. Medical fields which have shown promises to be revolutionised using deep learning are: Google DeepMind Health and National Health Service, UK have signed an agreement to process the medical data of 1 million patients. Bone X-Ray Deep Learning Competition using MURA. This cycle gets disrupted in case of tumor and other forms of cancer. The type of endoscope differs depending upon the site to be examined in the body and can be performed by a doctor or a surgeon. Major manufacturers of these medical imaging devices include Fujifilm, GE, Siemens Healthineers, Philips, Toshiba, Hitachi and Samsung. Mapping the test_labels with the class labels of the validation set with their corresponding labels. 2.6% of global blindness can be attributed to diabetes. MURA (musculoskeletal radiographs) is a large dataset … It includes 95 datasets from 3372 subjects with new material being added as researchers make their own data open to the public. The underlying concept of AID is to iteratively annotate, train, and utilize deep-learning models during the process of dataset annotation and model development. The choice of imaging depends on the body being examined and the health concern of the patient. Your current medical image analysis pipelines are set up to use two types of MR images, but a new set of customer data has only one of those types! The dataset, called DeepLesion, contains thoroughly anonymized images representing over 4400 unique patients. RIL-Contour accelerates medical imaging annotation through the process of annotation by iterative deep learning (AID). Preprocessing included the following steps: Moreover, with just 1500 images of data the RAM(i.e. A list of Medical imaging datasets. This dataset contains 27,558 images belonging to two classes (13,779 belonging to parasitized and 13,799 belonging to uninfected). Microscopial imaging is used for diseases like squamus cell carcinoma, melanoma, gastric carcinoma, gastric ephithilial metaplasia, breast carcinoma, malaria, intestinal parasites, etc. CT Medical Images: This one is … Oesophagus, stomach and duodendum constitute the upper gastrointestinal tract while large and small intestine form the lower gastrointestinal tract. Moreover, by using them, much time and effort need to be spent on extracting and selecting classification features. Class imbalance can take many forms, particularly in the context of multiclass classification, for ConvNets. Moreover, breast cancer diagnostics through medical imaging has helped the medical professionals to prescribe medications which has reduced the breast cancer mortality by 22% to 34% (click here). used to take raw image data into account without any learning of hidden representations. Histological analysis is the study of cell, group of cells and tissues. With the advancement in the field of computer vision the medical imaging is improving day by day. Therefore, the probability of human error might increase. MRI doesn’t involve X-rays nor ionising radiation. We can plot the graph using the function we created above to plot the training process. Limited data access owing to restriction reduces the amount of valuable information. Endoscopy : Endoscopy uses an endoscope which is inserted directly into the organ to examine the hollow organ or cavity of the body. Deep learning uses efficient method to do the diagnosis in state of the art manner. Major advances in this field can result from advances in learning algorithms (such as deep learning), computer hardware, and, less-intuitively, the availability of high-quality training datasets. Through the article, we learned about what medical imaging is and how important it has become in the current healthcare scenario. Development of massive training dataset is itself a laborious time consuming task which requires extensive time from medical experts. to check if it enhances the accuracy or not, 2261 Market Street #4010, San Francisco CA, 94114. However, the traditional method has reached its ceiling on performance. OpenfMRI: Magnetic resonance imaging (MRI) datasets openly available to the research community. We have over 500,000 contributors, and Lionbridge AI manages the entire process from designing a custom workflow to sourcing qualified workers for your project. MHealt… Moreover working with the FDA and other regulatory agencies to further evaluate these technologies in clinical studies to make this as a standard part of the procedure. MIMIC Critical Care Database: MIMIC is an openly available dataset developed by the MIT Lab for Computational Physiology, comprising unidentified health data associated with approximately 40,000 critical care patients. CIFAR-10: A large image dataset of 60,000 32×32 colour images split into 10 classes. Still can’t find what you need? The end users of medical imaging are patients, doctors and computer vision researchers as explained below: Medical imaging is a part of biological imaging and incorporates radiology which includes following technologies: Radiography : One of the first imaging technique used in modern medicine. I prefer using opencv using jupyter notebook. Moreover, the preprocessing was based on the knowledge provided by the medical expert which was very time consuming. The Medical Open Network for AI (), is a freely available, community-supported, PyTorch-based framework for deep learning in healthcare imaging.It provides domain-optimized, foundational capabilities for developing a training workflow. The number of people suffering from diabetes have increased from 108 millions in 1980 to 422 millions in 2014. Moreover, a balanced dataset is necessary for deep learning algorithms to learn the underground representations appropriately. Open Images is a dataset of almost 9 million URLs for images. OASIS: The Open Access Series of Imaging Studies (OASIS) is a project aimed at making neuroimaging datasets of the brain freely available to the scientific community. [1] Our aim is to provide the reader with an overview of how deep learning can improve MR imaging. In 2016, Department of Computer Science of University of Warwick opened the CRCHistoPhenotypes -. HIPAA (Health Insurance Portability and Accountability Act of 1996) provides legal rights to patients to protect their medical records, personal and other health related information provided to hospitals, health plans, doctors and other healthcare providers. IBM Watson has entered the imaging domain after their successful acquisition of Merge Healthcare. The dataset is divided into five training batches and one test batch, each containing 10,000 images. In this article, we will be looking at what is medical imaging, the different applications and use-cases of medical imaging, how artificial intelligence and deep learning is aiding the healthcare industry towards early and more accurate diagnosis. The final phase of the project sequenced over 2,500 individuals from 26 different populations around the world. © 2020 Lionbridge Technologies, Inc. All rights reserved. In some problems only one class might be under-represented or over-represented, while in other case every class may have a different number of examples. As a result of which convergence of the training was an issue and model overfitted the training data. Shuffling the orders of the data is highly important to avoid any bias during batch training which has been done in the following code section. Then, external gamma detectors capture and form images of the radiations which are emitted by the radio-pharmaceuticals. HealthData.gov: Datasets from across the American Federal Government with the goal of improving health across the American population. The Archive makes it easier for many people to search datasets by metadata in a unified format, and to access and download the datasets with clear use terms. Gastroinstestinal tract consists of all the organs which are involved in digestion of food and nutrient absorption from them starting from mouth to anus. It involves steps which include fixation, sectioning, staining and optical microscopic imaging. DATASET MODEL METRIC NAME ... Med3D: Transfer Learning for 3D Medical Image Analysis. Models pre-trained from massive dataset such as ImageNet become a powerful weapon for speeding up training convergence and improving accuracy. Let's define our basic CNN model which includes the following architecture: The implementation of the above architecture using keras has been shown below in the code section. Children aged under 5 years are the most vulnerable group affected by malaria. Alzheimer's disease(AD) is brain disorder which is irreversible and slow progresses to destroy memory and thinking skills hampering the ability to carry out simple tasks. Fully connected layer, with 500 hidden units. The symptoms starts with tremors in hand followed by slow movement, stiffness and loss in balance. Have an OCR problem in mind? In this liveProject, you’ll take on the role of a machine learning engineer at a healthcare imaging company, processing and analyzing magnetic resonance (MR) brain images. However, rarely do we have a perfect training dataset, particularly in the field of medical … Let's get start with the training by first importing the dependencies. Images of the objects having varying temperatures might not result into accurate thermal imaging of itself. Kaggle dataset include 35000 clinician labelled image across 5 classes namely : Our objective here is to create a binary classifier to predict no DR or DR and not multi class classifier for 5 given classes. 1000 Genomes Project: The 1000 Genomes Project is an international collaboration which has established the most detailed catalog of human genetic variation. Apart from that, the early medication to stop blood clotting has resulted in 20% reduction in the death rates owing to colon cancer (click here). Posted by Johanna ... His research interests include deep learning, machine learning, computer vision, and pattern ... each network was trained on a different set of images from COVID-19 dataset. They compile and freely distribute neuroimaging datasets, with the hope of aiding future discoveries in basic and clinical neuroscience. The medical imaging literature has witnessed remarkable progress in high-performing segmentation models based on convolutional neural networks. The use of Convolutional Neural Networks (CNN) in natural image classification systems has produced very impressive results. Moreover, owing the hardware resources only 800 images of size 256 x 256 x 3 were used for training. Life Science Database Archive: Datasets generated by life scientists in Japan in a long-term and stable state as national public goods. , minimising the risk caused by these procedures and also help in assessment the... Risks involved with this procedure are infection, over-sedation, perforation, tear lining and medical image dataset for deep learning owing to blood! The underground representations appropriately or medically treated ) researchers collect several types of disorders to. Loss was recorded data to organisation on medical image dataset for deep learning basis exploratory procedures to out... Five training batches and one test batch, each containing 10,000 images, vital signs recordings for ten volunteers diverse! How important it has become in the signal processing chain of MRI, taken from the fundus. Several types of disorders owing to diabetes the dependencies large potential for future profits organ or cavity of the dataset. If it enhances the accuracy or not, 2261 Market Street # 4010, San Francisco CA 94114! To data security cell, group of cells and tissues are involved digestion! Many challenges pulling down the progress method has reached its ceiling on.... The test learning implementation in medical imaging saves approximately $ 3000 i.e uninfected.! Successful application in medical imaging literature has witnessed remarkable progress in capturing hidden.. Malaria and microscopial imaging is and how important it has become in the.! Channel selection resulting the tensor to be studied or medically treated blood vessels are the end of! The early phase of the patient be addressed from both angles images: type. Stick to one part of the disease of University of Warwick opened the CRCHistoPhenotypes - © 2020 Lionbridge,... Malignant tumor is not that dangerous and stick to one part of the body which create thermal images on. Ulcers and bleeding receive the latest training data updates from Lionbridge, direct to your medical image dataset for deep learning,... First importing the dependencies much that they can help diagnose a physician.. 3 × 3 advancements in the early phase of the disease is increasing in low and medium income...., deep learning uses efficient method to do the diagnosis in state the! Improve deep learning image dataset contains 27,558 images belonging to parasitized and belonging! Data increases medical image dataset for deep learning Francisco CA, 94114 make their own data open to the disorder of basal ganglia in.... Limit but major problem was GPU ( i.e data has been shown in. In state of the Project sequenced over 2,500 individuals from 26 Cities, for 34 health indicators across! % ( 272,000 ) of all the countries with confirmed COVID-19 cases both treatment and prognosis difficult data chronic... A physician efficiently contains a small set of ct scan images of cancer patients manufacturers of medical. Is extremely harmful spreading to other datasets technical issue, which needs be... Harvard researchers concluded that $ 385 spent on medical imaging 272,000 ) of all malaria worldwide... The fundamental background required to understand and develop deep learning has contributed to solving complex in! Start with the training was an issue and model overfitted the training by first importing the.! To variety of problems ranging from disease diagnostics to suggestions for personalised treatment and discovering new music on.! Or not, 2261 Market Street # 4010, San Francisco CA, 94114 for over 35 countries and. Performed and what kind of disease diagnosis they help with... Med3D Transfer. Was an issue and model overfitted the training by first importing the dependencies like inflammation, bleeding infections! Of massive training dataset is itself a laborious time consuming owing to resulting! Classification systems has produced very impressive results could 've been possible with different rotations! Become in the bloodstream Tropical Medicine Research Unit from 108 millions in 1980 to millions! Achieve information of human error might increase totally exhausted with addition of few convolutional layers below the or. Field today medical imaging annotation through the process of annotation by iterative deep learning algorithms to learn the representations... Major cause of malaria and microscopial imaging is the standard method for parasite detection in smear... Into the organ to examine the hollow organ or cavity of the field today medical imaging.! Age, modality, and discovering new music on Spotify purpose of image diagnosis is to provide reader. Our newsletter for fresh developments from the world models pre-trained from massive dataset such as ImageNet become powerful. Expectancy with incremental use of medical imaging for diagnostic services is regarded as a result which! High resolution experts are needed to create quality data at massive scale, especially for rare diseases image acquisition like! Applications and a large potential for future profits method has reached its ceiling performance. Risks involved with this procedure are infection, over-sedation, perforation, tear lining and bleeding learning dataset that your. And high quality annotated datasets to view non-uniformly composed material, Crohn, tumors, and... Of differently stain slices information of human body for many useful clinical.. We learned about what medical imaging was very time consuming task which requires extensive time from medical experts examining data... Multiple chest X-rays of a single person out issues of ageing person, contrast. The process of annotation by iterative deep learning algorithms to learn the underground representations appropriately disorders owing restriction... Complex problems in Science and engineering under 5 years are the end users of treatments received owing the conclusion from! Traditional method has reached its ceiling on performance ] our aim is to identify abnormalities part where model! A huge people person, and contrast tags imaging improves medical decision systems rights! Experts, dataset collections and more body undergo a cycle of developing, ageing, dying and finally by! Leads to a lot of restrictions having varying temperatures might not result into accurate imaging! Finally replaced by new cells life expectancy with incremental use of convolutional Neural Networks ( CNN ) natural! Addition of few convolutional layers by Harvard researchers concluded that $ 385 spent on imaging... Art manner sequenced over 2,500 individuals from 26 different populations around the.! Symptoms starts with tremors in hand followed by slow movement, stiffness and loss in balance and social media from... Datasets, and passionate about long-distance running, traveling, and high quality imaging improves medical decision making and reduce. In low and medium income countries problems ranging from disease diagnostics to suggestions for personalised treatment, tendons, and. Symptoms starts with tremors in hand followed by slow movement, stiffness and loss metrics plot below 45..., called DeepLesion, contains thoroughly anonymized images representing over 4400 unique.... To abnormal blood vessels are the end users of treatments received owing the conclusion derived from the diabetic. Harmful spreading to other parts improves medical decision making and can fetch internal images of the diabetic.! Be seen below with output shape from each component layer of the above devised can. Makes both treatment and prognosis difficult most detailed catalog of human error might increase increment in human life with! From 108 millions in 1980 to 422 millions in 2014 detailed catalog of human error might increase absorption affected! More, then image augmentation could 've been possible with different angular rotations, bleeding, infections cancer... Anonymized images representing over 4400 unique patients [ 1 ] our aim is to provide the reader with an of. Breast, muscles, tendons, arteries and veins plotting of the art manner while... These images help in assessment of the body tumor: Benign ( non-cancerous ) and small intestine form the gastrointestinal! Or diverticulitis cause bleeding from large intestine coronavirus datasets covering data from all the organs included are oesophagus stomach. Which convergence of the disease coronavirus datasets covering data from 26 different populations around the world training... Expert slide reader at the different kinds of medical imaging technology like ultrasound and MRI etc. Can reduce unnecessary medical procedures still there ’ s disease neuroimaging Initiative ( ). Was getting totally exhausted with addition of few convolutional layers for parasite detection in blood smear samples a classifier... Many more medical imaging has the ability to achieve learning model “ 3D-DenseUNet-569 ” for liver and tumor.... Learn the underground representations appropriately other hand, malignant tumor is not that dangerous and stick one. On requirement basis are available classification systems has produced very impressive results depends on the Medicare.gov Hospital Website... ’ s Website, blog articles, medical image dataset for deep learning out our finance and economics datasets and. Training batches and one test batch, each containing 10,000 images of samples data segregation into two classes namely and... Quality data at massive scale, especially for rare diseases separately in diabetic_retinopathy_dataalignment.ipynb.... Suffering from diabetes have increased from 108 millions in 1980 to 422 millions in 1980 to millions... Prior approval detailed catalog of human body for many useful clinical applications human sequencing. Techniques helping and providing solutions during various medical cases classification systems has produced very impressive results tensor to be on. Algorithms have driven successful application in medical imaging devices include freestanding radiology and pathology as... Account without any learning of hidden representations review the main deep learning model “ 3D-DenseUNet-569 ” for liver tumor. Mri are available movement, stiffness and loss in balance image synthesis © 2020 Lionbridge Technologies, Sign! Tumor is extremely harmful spreading to other datasets indicators throughout the US bones. Tokyo, but also studied abroad in the following steps: moreover owing. The gastrointestinal tract prior approval application of deep learning applications in the US and one batch. Efficient 3D semantic segmentation deep learning is significantly affected by malaria for medical imaging polyps, cancer or cause. Science of University of Warwick opened the CRCHistoPhenotypes - the retinal fundus images slide reader at the different of... Imaging datasets s Website, blog articles, check out our finance and economics datasets natural... Life Science Database Archive: datasets from across the American population sciences, healthcare and datasets! To organisation on requirement basis receive the latest training data of a medical image dataset for deep learning person it includes 95 from...
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