Query: Transfer Learning

title: Exploring the Limits of Transfer Learning with a Unified Text to Text Transformer
abstract: Transfer learning, where a model is first pre trained on a data rich task before being fine tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP) The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text to text format. Our systematic study compares pre training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new "Colossal Clean Crawled Corpus" we achieve state of the art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre trained models, and code.

title: Covid 19: automatic detection from X ray images utilizing transfer learning with convolutional neural networks
abstract: In this study, a dataset of X ray images from patients with common bacterial pneumonia, confirmed Covid 19 disease, and normal incidents, was utilized for the automatic detection of the Coronavirus disease. The aim of the study is to evaluate the performance of state of the art convolutional neural network architectures proposed over the recent years for medical image classification. Specifically, the procedure called Transfer Learning was adopted. With transfer learning, the detection of various abnormalities in small medical image datasets is an achievable target, often yielding remarkable results. The datasets utilized in this experiment are two. Firstly, a collection of 1427 X ray images including 224 images with confirmed Covid 19 disease, 700 images with confirmed common bacterial pneumonia, and 504 images of normal conditions. Secondly, a dataset including 224 images with confirmed Covid 19 disease, 714 images with confirmed bacterial and viral pneumonia, and 504 images of normal conditions. The data was collected from the available X ray images on public medical repositories. The results suggest that Deep Learning with X ray imaging may extract significant biomarkers related to the Covid 19 disease, while the best accuracy, sensitivity, and specificity obtained is 96.78% 98.66% and 96.46% respectively. Since by now, all diagnostic tests show failure rates such as to raise concerns, the probability of incorporating X rays into the diagnosis of the disease could be assessed by the medical community, based on the findings, while more research to evaluate the X ray approach from different aspects may be conducted.

title: Deep COVID: Predicting COVID 19 from chest X ray images using deep transfer learning
abstract: The COVID 19 pandemic is causing a major outbreak in more than 150 countries around the world, having a severe impact on the health and life of many people globally. One of the crucial step in fighting COVID 19 is the ability to detect the infected patients early enough, and put them under special care. Detecting this disease from radiography and radiology images is perhaps one of the fastest ways to diagnose the patients. Some of the early studies showed specific abnormalities in the chest radiograms of patients infected with COVID 19. Inspired by earlier works, we study the application of deep learning models to detect COVID 19 patients from their chest radiography images. We first prepare a dataset of 5,000 Chest X rays from the publicly available datasets. Images exhibiting COVID 19 disease presence were identified by board certified radiologist. Transfer learning on a subset of 2,000 radiograms was used to train four popular convolutional neural networks, including ResNet18, ResNet50, SqueezeNet, and DenseNet 121, to identify COVID 19 disease in the analyzed chest X ray images. We evaluated these models on the remaining 3,000 images, and most of these networks achieved a sensitivity rate of 98% 3% while having a specificity rate of around 90% Besides sensitivity and specificity rates, we also present the receiver operating characteristic (ROC) curve, precision recall curve, average prediction, and confusion matrix of each model. We also used a technique to generate heatmaps of lung regions potentially infected by COVID 19 and show that the generated heatmaps contain most of the infected areas annotated by our board certified radiologist. While the achieved performance is very encouraging, further analysis is required on a larger set of COVID 19 images, to have a more reliable estimation of accuracy rates. The dataset, model implementations (in PyTorch) and evaluations, are all made publicly available for research community at https:/github.com/shervinmin/DeepCovid.git

title: A Novel Transfer Learning Based Approach for Pneumonia Detection in Chest X ray Images
abstract: Pneumonia is among the top diseases which cause most of the deaths all over the world. Virus, bacteria and fungi can all cause pneumonia. However, it is difficult to judge the pneumonia just by looking at chest X rays. The aim of this study is to simplify the pneumonia detection process for experts as well as for novices. We suggest a novel deep learning framework for the detection of pneumonia using the concept of transfer learning. In this approach, features from images are extracted using different neural network models pretrained on ImageNet, which then are fed into a classifier for prediction. We prepared five different models and analyzed their performance. Thereafter, we proposed an ensemble model that combines outputs from all pretrained models, which outperformed individual models, reaching the state of the art performance in pneumonia recognition. Our ensemble model reached an accuracy of 96.4% with a recall of 99.62% on unseen data from the Guangzhou Women and Children's Medical Center dataset.

title: Diagnosing COVID 19 pneumonia from x ray and CT images using deep learning and transfer learning algorithms
abstract: The novel coronavirus 2019 (COVID 19) first appeared in Wuhan province of China and spread quickly around the globe and became a pandemic. The gold standard for confirming COVID 19 infection is through Reverse Transcription Polymerase Chain Reaction (RT PCR) assay. The lack of sufficient RT PCR testing capacity, false negative results of RT PCR, time to get back the results and other logistical constraints enabled the epidemic to continue to spread albeit interventions like regional or complete country lockdowns. Therefore, chest radiographs such as CT and X ray can be used to supplement PCR in combating the virus from spreading. In this work, we focus on proposing a deep learning tool that can be used by radiologists or healthcare professionals to diagnose COVID 19 cases in a quick and accurate manner. However, the lack of a publicly available dataset of X ray and CT images makes the design of such AI tools a challenging task. To this end, this study aims to build a comprehensive dataset of X rays and CT scan images from multiple sources as well as provides a simple but an effective COVID 19 detection technique using deep learning and transfer learning algorithms. In this vein, a simple convolution neural network (CNN) and modified pre trained AlexNet model are applied on the prepared X rays and CT scan images. The result of the experiments shows that the utilized models can provide accuracy up to 98% via pre trained network and 94.1% accuracy by using the modified CNN.

title: Within the Lack of Chest COVID 19 X ray Dataset: A Novel Detection Model Based on GAN and Deep Transfer Learning
abstract: The coronavirus (COVID 19) pandemic is putting healthcare systems across the world under unprecedented and increasing pressure according to the World Health Organization (WHO) With the advances in computer algorithms and especially Artificial Intelligence, the detection of this type of virus in the early stages will help in fast recovery and help in releasing the pressure off healthcare systems. In this paper, a GAN with deep transfer learning for coronavirus detection in chest X ray images is presented. The lack of datasets for COVID 19 especially in chest X rays images is the main motivation of this scientific study. The main idea is to collect all the possible images for COVID 19 that exists until the writing of this research and use the GAN network to generate more images to help in the detection of this virus from the available X rays images with the highest accuracy possible. The dataset used in this research was collected from different sources and it is available for researchers to download and use it. The number of images in the collected dataset is 307 images for four different types of classes. The classes are the COVID 19, normal, pneumonia bacterial, and pneumonia virus. Three deep transfer models are selected in this research for investigation. The models are the Alexnet, Googlenet, and Restnet18. Those models are selected for investigation through this research as it contains a small number of layers on their architectures, this will result in reducing the complexity, the consumed memory and the execution time for the proposed model. Three case scenarios are tested through the paper, the first scenario includes four classes from the dataset, while the second scenario includes 3 classes and the third scenario includes two classes. All the scenarios include the COVID 19 class as it is the main target of this research to be detected. In the first scenario, the Googlenet is selected to be the main deep transfer model as it achieves 80.6% in testing accuracy. In the second scenario, the Alexnet is selected to be the main deep transfer model as it achieves 85.2% in testing accuracy, while in the third scenario which includes two classes (COVID 19, and normal) Googlenet is selected to be the main deep transfer model as it achieves 100% in testing accuracy and 99.9% in the validation accuracy. All the performance measurement strengthens the obtained results through the research.

title: A Secure Federated Transfer Learning Framework
abstract: Machine learning relies on the availability of vast amounts of data for training. However, in reality, data are mostly scattered across different organizations and cannot be easily integrated due to many legal and practical constraints. To address this important challenge in the field of machine learning, we introduce a new technique and framework, known as federated transfer learning (FTL) to improve statistical modeling under a data federation. FTL allows knowledge to be shared without compromising user privacy and enables complementary knowledge to be transferred across domains in a data federation, thereby enabling a target domain party to build flexible and effective models by leveraging rich labels from a source domain. This framework requires minimal modifications to the existing model structure and provides the same level of accuracy as the nonprivacy preserving transfer learning. It is flexible and can be effectively adapted to various secure multiparty machine learning tasks.

title: A transfer learning method with deep residual network for pediatric pneumonia diagnosis
abstract: Computer aided diagnosis systems based on deep learning and medical imaging is increasingly becoming research hotspots. At the moment, the classical convolutional neural network generates classification results by hierarchically abstracting the original image. These abstract features are less sensitive to the position and orientation of the object, and this lack of spatial information limits the further improvement of image classification accuracy. Therefore, how to develop a suitable neural network framework and training strategy in practical clinical applications to avoid this problem is a topic that researchers need to continue to explore.  We propose a deep learning framework that combines residual thought and dilated convolution to diagnose and detect childhood pneumonia. Specifically, based on an understanding of the nature of the child pneumonia image classification task, the proposed method uses the residual structure to overcome the over fitting and the degradation problems of the depth model, and utilizes dilated convolution to overcome the problem of loss of feature space information caused by the increment in depth of the model. Furthermore, in order to overcome the problem of difficulty in training model due to insufficient data and the negative impact of the introduction of structured noise on the performance of the model, we use the model parameters learned on large scale datasets in the same field to initialize our model through transfer learning.  Our proposed method has been evaluated for extracting texture features associated with pneumonia and for accurately identifying the performance of areas of the image that best indicate pneumonia. The experimental results of the test dataset show that the recall rate of the method on children pneumonia classification task is 96.7% and the f1 score is 92.7% Compared with the prior art methods, this approach can effectively solve the problem of low image resolution and partial occlusion of the inflammatory area in children chest X ray images.  The novel framework focuses on the application of advanced classification that directly performs lesion characterization, and has high reliability in the classification task of children pneumonia.

title: Diagnosing Rotating Machines With Weakly Supervised Data Using Deep Transfer Learning
abstract: Rotating machinery fault diagnosis problems have been well addressed when sufficient supervised data of the tested machine are available using the latest data driven methods. However, it is still challenging to develop effective diagnostic method with insufficient training data, which is highly demanded in real industrial scenarios, since high quality data are usually difficult and expensive to collect. Considering the underlying similarities of rotating machines, data mining on different but related equipments potentially benefit the diagnostic performance on the target machine. Therefore, a novel transfer learning method for diagnostics based on deep learning is proposed in this article, where the diagnostic knowledge learned from sufficient supervised data of multiple rotating machines is transferred to the target equipment with domain adversarial training. Different from the existing studies, a more generalized transfer learning problem with different label spaces of domains is investigated, and different fault severities are also considered in fault diagnostics. The experimental results on four datasets validate the effectiveness of the proposed method, and show it is feasible and promising to explore different datasets to improve diagnostic performance.

title: Detection of Coronavirus (COVID 19) Associated Pneumonia based on Generative Adversarial Networks and a Fine Tuned Deep Transfer Learning Model using Chest X ray Dataset
abstract: The COVID 19 coronavirus is one of the devastating viruses according to the world health organization. This novel virus leads to pneumonia, which is an infection that inflames the lungs' air sacs of a human. One of the methods to detect those inflames is by using x rays for the chest. In this paper, a pneumonia chest x ray detection based on generative adversarial networks (GAN) with a fine tuned deep transfer learning for a limited dataset will be presented. The use of GAN positively affects the proposed model robustness and made it immune to the overfitting problem and helps in generating more images from the dataset. The dataset used in this research consists of 5863 X ray images with two categories: Normal and Pneumonia. This research uses only 10% of the dataset for training data and generates 90% of images using GAN to prove the efficiency of the proposed model. Through the paper, AlexNet, GoogLeNet, Squeeznet, and Resnet18 are selected as deep transfer learning models to detect the pneumonia from chest x rays. Those models are selected based on their small number of layers on their architectures, which will reflect in reducing the complexity of the models and the consumed memory and time. Using a combination of GAN and deep transfer models proved it is efficiency according to testing accuracy measurement. The research concludes that the Resnet18 is the most appropriate deep transfer model according to testing accuracy measurement and achieved 99% with the other performance metrics such as precision, recall, and F1 score while using GAN as an image augmenter. Finally, a comparison result was carried out at the end of the research with related work which used the same dataset except that this research used only 10% of original dataset. The presented work achieved a superior result than the related work in terms of testing accuracy.