Training certified detectives to track down the intrinsic shortcuts in COVID-19 chest x-ray data sets

Deep learning faces a significant challenge wherein the trained models often underperform when used with external test data sets. This issue has been attributed to spurious correlations between irrelevant features in the input data and corresponding labels. This study uses the classification of COVID-19 from chest x-ray radiographs as an example to demonstrate that the image contrast and sharpness, which are characteristics of a chest radiograph dependent on data acquisition systems and imaging parameters, can be intrinsic shortcuts that impair the model\u27s generalizability. The study proposes training certified shortcut detective models that meet a set of qualification criteria which can then identify these intrinsic shortcuts in a curated data set

Detección automática de neumonía para dar soporte al diagnóstico de la COVID-19

[ES] El análisis de imagen médica es, cada vez más, una herramienta que se está convirtiendo en un apoyo para el personal médico a la hora de emitir un diagnóstico. En este trabajo, abordamos el tema del análisis de imagen médica para tratar de resolver el problema de la detección de neumonía por COVID-19 en diferentes tipos de imagen médica como por ejemplo imágenes Rayos-X o imágenes por resonancia magnética. Para conseguir los objetivos se propondrán modelos ad-hoc y otros que han sido publicados y se tratará de hacer una comparativa entre modelos y tipos de datos para extraer conclusiones.[CA] L’anàlisi d’imatge mèdica és, cada vegada més, una eina que s’està convertint en un suport per als metges a l’hora d’emetre un diagnòstic. En aquest treball, abordem el tema de l’anàlisi d’imatge mèdica per tractar de resoldre el problema de la detecció de pneumònia per COVID-19 a diferents tipus d’imatge mèdica, com ara imatges Raigs-X o imatges per resonància magnètica. Per a aconseguir els objectius es proposaràn models ad-hoc i altres que han sigut publicats i es tractará de fer una comparativa entre models i entre tipus de dades per tal de extraure conclusions.[EN] Medical imaging analysis is a tool which is increasingly becoming a support for doctors when it comes the time to issue a diagnosis. In this work, we address the medical images analysis topic aiming to solve the problem of detecting COVID-19 pneumonia in different types of medical images, such as X-Ray or magnetic resonance images. To achieve the proposed objectives, ad-hoc models are proposed and already published models are also used. In this work, different models and type of images are compared so that some conclussions are reached.Sánchez Romero, RV. (2020). Detección automática de neumonía para dar soporte al diagnóstico de la COVID-19. Universitat Politècnica de València. http://hdl.handle.net/10251/159154TFG

Cov-caldas: A new COVID-19 chest X-Ray dataset from state of Caldas-Colombia

The emergence of COVID-19 as a global pandemic forced researchers worldwide in various disciplines to investigate and propose efficient strategies and/or technologies to prevent COVID-19 from further spreading. One of the main challenges to be overcome is the fast and efficient detection of COVID-19 using deep learning approaches and medical images such as Chest Computed Tomography (CT) and Chest X-ray images. In order to contribute to this challenge, a new dataset was collected in collaboration with “S.E.S Hospital Universitario de Caldas” (https://hospitaldecaldas.com/) from Colombia and organized following the Medical Imaging Data Structure (MIDS) format. The dataset contains 7,307 chest X-ray images divided into 3,077 and 4,230 COVID-19 positive and negative images. Images were subjected to a selection and anonymization process to allow the scientific community to use them freely. Finally, different convolutional neural networks were used to perform technical validation. This dataset contributes to the scientific community by tackling significant limitations regarding data quality and availability for the detection of COVID-19. © 2022, The Author(s)

A comparison of Covid-19 early detection between convolutional neural networks and radiologists

[EN] Background The role of chest radiography in COVID-19 disease has changed since the beginning of the pandemic from a diagnostic tool when microbiological resources were scarce to a different one focused on detecting and monitoring COVID-19 lung involvement. Using chest radiographs, early detection of the disease is still helpful in resource-poor environments. However, the sensitivity of a chest radiograph for diagnosing COVID-19 is modest, even for expert radiologists. In this paper, the performance of a deep learning algorithm on the first clinical encounter is evaluated and compared with a group of radiologists with different years of experience. Methods The algorithm uses an ensemble of four deep convolutional networks, Ensemble4Covid, trained to detect COVID-19 on frontal chest radiographs. The algorithm was tested using images from the first clinical encounter of positive and negative cases. Its performance was compared with five radiologists on a smaller test subset of patients. The algorithm's performance was also validated using the public dataset COVIDx. Results Compared to the consensus of five radiologists, the Ensemble4Covid model achieved an AUC of 0.85, whereas the radiologists achieved an AUC of 0.71. Compared with other state-of-the-art models, the performance of a single model of our ensemble achieved nonsignificant differences in the public dataset COVIDx. Conclusion The results show that the use of images from the first clinical encounter significantly drops the detection performance of COVID-19. The performance of our Ensemble4Covid under these challenging conditions is considerably higher compared to a consensus of five radiologists. Artificial intelligence can be used for the fast diagnosis of COVID-19.Project Chest screening for patients with COVID 19 (COV2000750 Special COVID19 resolution) funded by Instituto de Salud Carlos III. Project DIRAC (INNVA1/2020/42) funded by the Agencia Valenciana de la Innovacion, Generalitat Valenciana.Albiol Colomer, A.; Albiol, F.; Paredes Palacios, R.; Plasencia-Martínez, JM.; Blanco Barrio, A.; García Santos, JM.; Tortajada, S.... (2022). A comparison of Covid-19 early detection between convolutional neural networks and radiologists. Insights into Imaging. 13(1):1-12. https://doi.org/10.1186/s13244-022-01250-311213