AI-MIA: COVID-19 Detection and Severity Analysis Through Medical Imaging

This paper presents the baseline approach for the organized 2nd Covid-19 Competition, occurring in the framework of the AIMIA Workshop in the European Conference on Computer Vision (ECCV 2022). It presents the COV19-CT-DB database which is annotated for COVID-19 detection, consisting of about 7,700 3-D CT scans. Part of the database consisting of Covid-19 cases is further annotated in terms of four Covid-19 severity conditions. We have split the database and the latter part of it in training, validation and test datasets. The former two datasets are used for training and validation of machine learning models, while the latter is used for evaluation of the developed models. The baseline approach consists of a deep learning approach, based on a CNN-RNN network and report its performance on the COVID19-CT-DB database. The paper presents the results of both Challenges organised in the framework of the Competition, also compared to the performance of the baseline scheme. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A Large Imaging Database and Novel Deep Neural Architecture for Covid-19 Diagnosis

Deep learning methodologies constitute nowadays the main approach for medical image analysis and disease prediction. Large annotated databases are necessary for developing these methodologies;such databases are difficult to obtain and to make publicly available for use by researchers and medical experts. In this paper, we focus on diagnosis of Covid-19 based on chest 3-D CT scans and develop a dual knowledge framework, including a large imaging database and a novel deep neural architecture. We introduce COV19-CT-DB, a very large database annotated for COVID-19 that consists of 7,750 3-D CT scans, 1,650 of which refer to COVID-19 cases and 6,100 to non-COVID19 cases. We use this database to train and develop the RACNet architecture. This architecture performs 3-D analysis based on a CNN-RNN network and handles input CT scans of different lengths, through the introduction of dynamic routing, feature alignment and a mask layer. We conduct a large experimental study that illustrates that the RACNet network has the best performance compared to other deep neural networks i) when trained and tested on COV19-CT-DB;ii) when tested, or when applied, through transfer learning, to other public databases. © 2022 IEEE.

Extracting geographical characteristics about COVID-19 evolution worldwide using machine learning

Since the beginning of 2020, the whole world has been plagued by the coronavirus pandemic. During the last sixteen months, almost every country in the world has faced several epidemic waves. An intriguing question that arises is whether neighboring countries, similar in regard to their socioeconomic status and the restrictions employed to counter the spread of the virus, showcase similarities in their respective number of cases and deaths. To that end, in this paper we form three clusters of similar countries (European and USA, African-Asian and Latin American) and we use their cumulative data as training data for machine learning models (RNN family, TCN and Attention) that predict the respective cases and deaths of 4 fixed neighboring countries, namely Cyprus, Greece, Italy and Spain. The results of the experiments conducted show that these 4 countries accent bigger similarity with the European cluster, as expected. Thus, evidence is provided bolstering the claim that similar neighboring countries exhibit alike behavior regarding the repercussions of the COVID-19. © 2021 IEEE.

Effects of image quality and quantity on building a competitive COVID-19 diagnosis model

The outbreak of a health crisis, such as Covid-19, leads to decisions that must combine efficiency and speed. Often there is a trade-off between these two values, as the faster a decision is made, the less information is considered. This paper presents a deep learning model pipeline that balances these two values with the primary goal of classifying human lung X-rays into three categories: pneumonia, covid-19 and normal. Through this process, we tried to explore whether the quality of an image can enhance the learning process to a greater extent as opposed to having larger number of images. For this purpose, we follow two approaches by viewing quality and quantity as competing objectives to increasing the level of information obtained. The first is through increasing the number of X-ray images in the dataset, and the second is through improving the quality of the X-ray images. In the first approach, our goal is achieved using a Generative Adversarial Network (GAN) to generate plasmatic covid-19 class X-rays, while in the second approach, we improve the resolution of the X-ray images. To find the hyperparameters in both approaches that lead to better system performance, we exploit the Particle Swarm Optimization (PSO) algorithm. Rapid training and hyperparameter tuning better perform through this algorithm. Our experiments depict the performance that our models, based on the two approaches, achieved. Accuracy reaches 93% while sensitivity reaches 90% over Covid-19 cases. Finally, we conclude which characteristic, quality or quantity, is most useful in our case. © 2021 IEEE.

MIA-COV19D: COVID-19 Detection through 3-D Chest CT Image Analysis

Early and reliable COVID-19 diagnosis based on chest 3-D CT scans can assist medical specialists in vital circumstances. Deep learning methodologies constitute a main approach for chest CT scan analysis and disease prediction. However, large annotated databases are necessary for developing deep learning models that are able to provide COVID-19 diagnosis across various medical environments in different countries. Due to privacy issues, publicly available COVID-19 CT datasets are highly difficult to obtain, which hinders the research and development of AI-enabled diagnosis methods of COVID-19 based on CT scans. In this paper we present the COV19-CT-DB database which is annotated for COVID-19, consisting of about 5,000 3-D CT scans, We have split the database in training, validation and test datasets. The former two datasets can be used for training and validation of machine learning models, while the latter will be used for evaluation of the developed models. We present a deep learning approach, based on a CNN-RNN network and report its performance on the COVID19-CT-DB database. Moreover, we present the results of all main techniques that were developed and used in the ICCV COV19D Competition.

Transparent Adaptation in Deep Medical Image Diagnosis

The paper presents a novel deep learning approach, which extracts latent information from trained Deep Neural Networks (DNNs) and derives concise representations that are analyzed in an effective, transparent way for prediction in medical imaging. A novel methodology is presented, in which deep neural architectures that have been trained to provide highly accurate predictions over existing datasets are adapted, in a consistent way, to make predictions over different contexts and datasets. Unified prediction is then achieved over the original and the new datasets. Successful application is illustrated through a large experimental study for prediction of Parkinson’s disease from MRI and DaTScans, as well as for prediction of COVID-19 from CT scans and X-rays. © 2021, Springer Nature Switzerland AG.