Deep transfer learning application for automated ischemic classification in posterior fossa CT images

Abstract—Computed Tomography (CT) imaging is one of the conventional tools used to diagnose ischemic in Posterior Fossa (PF). Radiologist commonly diagnoses ischemic in PF through CT imaging manually. However, such a procedure could be strenuous and time consuming for large scale images, depending on the expertise and ischemic visibility. With the rapid development of computer technology, automatic image classification based on Machine Learning (ML) is widely been developed as a second opinion to the ischemic diagnosis. The practical performance of ML is challenged by the emergence of deep learning applications in healthcare. In this study, we evaluate the performance of deep transfer learning models of Convolutional Neural Network (CNN); VGG-16, GoogleNet and ResNet-50 to classify the normal and abnormal (ischemic) brain CT images of PF. This is the first study that intensively studies the application of deep transfer learning for automated ischemic classification in the posterior part of brain CT images. The experimental results show that ResNet-50 is capable to achieve the highest accuracy performance in comparison to other proposed models. Overall, this automatic classification provides a convenient and time-saving tool for improving medical diagnosis

Convolutional Neural Network for Stereotypical Motor Movement Detection in Autism

Autism Spectrum Disorders (ASDs) are often associated with specific atypical postural or motor behaviors, of which Stereotypical Motor Movements (SMMs) have a specific visibility. While the identification and the quantification of SMM patterns remain complex, its automation would provide support to accurate tuning of the intervention in the therapy of autism. Therefore, it is essential to develop automatic SMM detection systems in a real world setting, taking care of strong inter-subject and intra-subject variability. Wireless accelerometer sensing technology can provide a valid infrastructure for real-time SMM detection, however such variability remains a problem also for machine learning methods, in particular whenever handcrafted features extracted from accelerometer signal are considered. Here, we propose to employ the deep learning paradigm in order to learn discriminating features from multi-sensor accelerometer signals. Our results provide preliminary evidence that feature learning and transfer learning embedded in the deep architecture achieve higher accurate SMM detectors in longitudinal scenarios.Comment: Presented at 5th NIPS Workshop on Machine Learning and Interpretation in Neuroimaging (MLINI), 2015, (http://arxiv.org/html/1605.04435), Report-no: MLINI/2015/1

Improving mental health using machine learning to assist humans in the moderation of forum posts

This work investigates the potential for the application of machine learning and natural language processing technology in an online application designed to help teenagers talk about their mental health issues. Specifically, we investigate whether automatic classification methods can be applied with sufficient accuracy to assist humans in the moderation of posts and replies to an online forum. Using real data from an existing application, we outline the specific problems of lack of data, class imbalance and multiple rejection reasons. We investigate a number of machine learning architectures including a state-of-the-art transfer learning architecture, BERT, which has performed well elsewhere despite limited training data, due to its use of pre-training on a very large general corpus. Evaluating on real data, we demonstrate that further large performance gains can be made through the use of automatic data augmentation techniques (synonym replacement, synonym insertion, random swap and random deletion). Using a combination of data augmentation and transfer learning, performance of the automatic classification rivals human performance at the task, thus demonstrating the feasibility of deploying these techniques in a live system

Deep Convolutional Neural Networks for Interpretable Analysis of EEG Sleep Stage Scoring

Sleep studies are important for diagnosing sleep disorders such as insomnia, narcolepsy or sleep apnea. They rely on manual scoring of sleep stages from raw polisomnography signals, which is a tedious visual task requiring the workload of highly trained professionals. Consequently, research efforts to purse for an automatic stage scoring based on machine learning techniques have been carried out over the last years. In this work, we resort to multitaper spectral analysis to create visually interpretable images of sleep patterns from EEG signals as inputs to a deep convolutional network trained to solve visual recognition tasks. As a working example of transfer learning, a system able to accurately classify sleep stages in new unseen patients is presented. Evaluations in a widely-used publicly available dataset favourably compare to state-of-the-art results, while providing a framework for visual interpretation of outcomes.Comment: 8 pages, 1 figure, 2 tables, IEEE 2017 International Workshop on Machine Learning for Signal Processin

Evolution and scientific visualization of Machine learning field

[EN] This article provides a retrospective and understanding of the development of automatic learning methods. The beginnings are visualized as a discipline within Computer Sciences in the subcategory of Artificial Intelligence, its development and the current transfer of knowledge to other areas of Engineering and its industrial applications. Based on the publications about machine learning and its application contained in the Web of Science database, records from 1986 to 2017 are downloaded. After a description of the technological profile, a new approach is introduced to the classification of a discipline based on the year of appearance of those terms that define it. Mining of technological texts and network theory has been applied to extract the terms and interpret their evolution. They are the those that define the stages of emergence, development and maturation of the discipline Machine learning. The novelty of this approach lies in the technical nature of applied research in Machine Learning, which aims to be a guide for the development of future engineering applications and to make technology transfer toindustry visible.Río-Belver, R.; Garechana, G.; Bildosola, I.; Zarrabeitia, E. (2018). Evolution and scientific visualization of Machine learning field. En 2nd International Conference on Advanced Reserach Methods and Analytics (CARMA 2018). Editorial Universitat Politècnica de València. 115-123. https://doi.org/10.4995/CARMA2018.2018.8329OCS11512

Computational Intelligent Models for Alzheimer's Prediction Using Audio Transcript Data

Alzheimer's dementia (AD) is characterized by memory loss, which is one of the earliest symptoms to develop. In this study, we investigated audio transcript data of patients with Alzheimer's dementia. The study involved the use of three intelligent computational approaches: conventional machine learning (Support Vector Machine, Random Forest, Decision Tree), sequential deep learning (LSTM, bidirectional LSTM, CNN-LSTM), and transfer learning (BERT, XLNet) models for automatic detection of linguistic indicators for early diagnosis of Alzheimer's dementia. These models were trained on the DementiaBank clinical transcript dataset. The grid search tuning approach is used for tuning the values of the hyperparameters. Text vectorization is done using the Term Frequency-Inverse Document Frequency (TF-IDF) information retrieval approach. TF-IDF is based on the Bag of Words (BoW) paradigm, which deals with the less and more relevant words in a transcript. Results were evaluated and compared using several performance metrics. The state-of-the-art techniques implemented on DementiaBank dataset in our methodology achieved better performance in terms of accuracy. Transfer learning models showed better classification results in comparison to sequential deep learning models. However, sequential deep learning models outperformed traditional machine learning models. Overall, in terms of accuracy, BERT and XLNet were the most accurate, with accuracy of 93 % and 92 %, respectively