A key factor in the fight against viral diseases such as the coronavirus (COVID-19) is the identification of virus carriers as early and quickly as possible, in a cheap and efficient manner. The application of deep learning for image classification of chest X-ray images of COVID-19 patients could become a useful pre-diagnostic detection methodology. However, deep learning architectures require large labelled datasets. This is often a limitation when the subject of research is relatively new as in the case of the virus outbreak, where dealing with small labelled datasets is a challenge. Moreover, in such context, the datasets are also highly imbalanced, with few observations from positive cases of the new disease. In this work we evaluate the performance of the semi-supervised deep learning architecture known as MixMatch with a very limited number of labelled observations and highly imbalanced labelled datasets. We demonstrate the critical impact of data imbalance to the model’s accuracy. Therefore, we propose a simple approach for correcting data imbalance, by re-weighting each observation in the loss function, giving a higher weight to the observations corresponding to the under-represented class. For unlabelled observations, we use the pseudo and augmented labels calculated by MixMatch to choose the appropriate weight. The proposed method improved classification accuracy by up to 18%, with respect to the non balanced MixMatch algorithm. We tested our proposed approach with several available datasets using 10, 15 and 20 labelled observations, for binary classification (COVID-19 positive and normal cases). For multi-class classification (COVID-19 positive, pneumonia and normal cases), we tested 30, 50, 70 and 90 labelled observations. Additionally, a new dataset is included among the tested datasets, composed of chest X-ray images of Costa Rican adult patients.

is spreading fast, infecting people through contact in various forms including droplets from sneezing and coughing. Therefore, the detection of infected subjects in an early, quick and cheap manner is urgent. Currently available tests are scarce and limited to people in danger of serious illness. The application of deep learning to chest Xray images for Covid-19 detection is an attractive approach. However, this technology usually relies on the availability of large labelled datasets, a requirement hard to meet in the context of a virus outbreak. To overcome this challenge, a semi-supervised deep learning model using both labelled and unlabelled data is proposed. We develop and test a semi-supervised deep learning framework based on the Mix Match architecture to classify chest X-rays into Covid-19, pneumonia and healthy cases. The presented approach was calibrated using two publicly available datasets. The results show an accuracy increase of around 15% under low labelled / unlabelled data ratio. This indicates that our semi-supervised framework can help improve performance levels towards Covid-19 detection when the amount of high-quality labelled data is scarce. Also, we introduce a semi-supervised deep learning boost coefficient which is meant to ease the scalability of our approach and performance comparison.

In this work we implement a COVID-19 infection detection system based on chest Xray images with uncertainty estimation. Uncertainty estimation is vital for safe usage of computer aided diagnosis tools in medical applications. Model estimations with high uncertainty should be carefully analyzed by a trained radiologist. We aim to improve uncertainty estimations using unlabelled data through the MixMatch semi-supervised framework. We test popular uncertainty estimation approaches, comprising Softmax scores, Monte-Carlo dropout and deterministic uncertainty quantification. To compare the reliability of the uncertainty estimates, we propose the usage of the Jensen-Shannon distance between the uncertainty distributions of correct and incorrect estimations. This metric is statistically relevant, unlike most previously used metrics, which often ignore the distribution of the uncertainty estimations. Our test results show a significant improvement in uncertainty estimates when using unlabelled data. The best results are obtained with the use of the Monte Carlo dropout method.

The main objective of the paper is to provide a like-with-like performance comparison between the wavelet domain and the multiwavelet domain watermarking, under a variety of attacks. The investigation is restricted to balanced multiwavelets. Furthermore, for multiwavelet domain watermarking, both wavelet-style and multiwavelet-style embedding are investigated. It was shown that none of the investigated techniques performs best across the board. The waveletstyle multiwavelet technique is best suited for compression attacks, whereas scalar wavelets are superior under cropping and scaling. The multiwavelet-style multiwavelet is far superior under low-pass filtering. On the basis of these results, it was concluded that for attacks which are likely to affect mid-range frequencies, the wavelets are more suitable than multiwavelets, whereas for attacks which are likely to affect low frequencies or high frequencies, the multiwavelets are the best choice. Furthermore, the multiwavelets generally offer better visual quality than scalar wavelets, for the same peak signal-to-noise ratio (PSNR). This suggests that part of the available channel capacity remains unused, and shows once more the potential of multiwavelets for digital watermarking.

Computer aided diagnosis for mammogram images have seen positive results through the usage of deep learning architectures. However, limited sample sizes for the target datasets might prevent the usage of a deep learning model under real world scenarios. The usage of unlabeled data to improve the accuracy of the model can be an approach to tackle the lack of target data. Moreover, important model attributes for the medical domain as model uncertainty might be improved through the usage of unlabeled data. Therefore, in this work we explore the impact of using unlabeled data through the implementation of a recent approach known as MixMatch, for mammogram images. We evaluate the improvement on accuracy and uncertainty of the model using popular and simple approaches to estimate uncertainty. For this aim, we propose the usage of the uncertainty balanced accuracy metric.

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The recent increase in ageing population in countries around the world has brought a lot of attention toward research and development of ambient assisted living (AAL) systems. These systems should be inexpensive to be installed in elderly homes, protecting their privacy and more importantly being non-invasive and smart. In this paper, we introduce an inexpensive system that utilises off-the-shelf sensor to grab RGB-D data. This data is then fed into different learning algorithms for classification different activity types. We achieve a very good success rate (99.9%) for human activity recognition (HAR) with the help of light-weighted and fast random forests (RF).

Photostimulable Phosphor Plates are commonly used in digital X-ray imaging for dentistry. During its usage, these plates get damaged, influencing the diagnosis performance and confidence of the dentistry professional. We propose a deep learning based classifier to discard or extend the use of photostimulable phosphor (PSP) plates based on their physical damage. The system automatically assesses, for the first time in the literature, when dentists should discard their plates. To validate our methodology, an in-house dataset is built on 25 PSP artifact masks (Carestream, CS 7600) digitally superimposed over 100 Complementary Metal-oxide-semiconductor (CMOS) periapical images (Carestream, RVG 6200) with known radiologic interpretations. From these 2500 images, unique subsets of 100 images were evaluated by 25 dentists to find periapical inflammatory lesions on the tooth. Doctors' opinion on whether the plates should be discarded or not was also collected. Stateof-the-art deep convolutional networks were tested using fivefold cross validation, yielding classification accuracies from 87% to almost 89%. Specifically, InceptionV3 and Resnet50 obtained the best performances with statistical significance. Qualitative heat-maps showed that such models can identify and employ artifacts to decide on whether to discard the PSP plate or not.