Клинические аспекты применения искусственного интеллекта для интерпретации рентгенограмм органов грудной клетки

The review considers the possible use of artificial intelligence for the interpretation of chest X-rays by analyzing 45 publications. Experimental and commercial diagnostic systems for pulmonary tuberculosis, pneumonia, neoplasms and other diseases have been analyzed.В обзоре рассмотрены возможности применения искусственного интеллекта для интерпретации рентгенограмм органов грудной клетки путем анализа 45 литературных источников. Проанализированы экспериментальные и коммерческие системы диагностики туберкулеза легких, пневмоний, новообразований и других заболеваний

Deep Learning in Chest Radiography: From Report Labeling to Image Classification

Chest X-ray (CXR) is the most common examination performed by a radiologist. Through CXR, radiologists must correctly and immediately diagnose a patient’s thorax to avoid the progression of life-threatening diseases. Not only are certified radiologists hard to find but also stress, fatigue, and lack of experience all contribute to the quality of an examination. As a result, providing a technique to aid radiologists in reading CXRs and a tool to help bridge the gap for communities without adequate access to radiological services would yield a huge advantage for patients and patient care. This thesis considers one essential task, CXR image classification, with Deep Learning (DL) technologies from the following three aspects: understanding the intersection of CXR interpretation and DL; extracting multiple image labels from radiology reports to facilitate the training of DL classifiers; and developing CXR classifiers using DL. First, we explain the core concepts and categorize the existing data and literature for researchers entering this field for ease of reference. Using CXRs and DL for medical image diagnosis is a relatively recent field of study because large, publicly available CXR datasets have not been around for very long. Second, we contribute to labeling large datasets with multi-label image annotations extracted from CXR reports. We describe the development of a DL-based report labeler named CXRlabeler, focusing on inductive sequential transfer learning. Lastly, we explain the design of three novel Convolutional Neural Network (CNN) classifiers, i.e., MultiViewModel, Xclassifier, and CovidXrayNet, for binary image classification, multi-label image classification, and multi-class image classification, respectively. This dissertation showcases significant progress in the field of automated CXR interpretation using DL; all source code used is publicly available. It provides methods and insights that can be applied to other medical image interpretation tasks

Identification of Pneumonia Disease Applying an Intelligent Computational Framework Based on Deep Learning and Machine Learning Techniques

Pneumonia is a very common and fatal disease, which needs to be identified at the initial stages in order to prevent a patient having this disease from more damage and help him/her in saving his/her life. Various techniques are used for the diagnosis of pneumonia including chest X-ray, CT scan, blood culture, sputum culture, fluid sample, bronchoscopy, and pulse oximetry. Medical image analysis plays a vital role in the diagnosis of various diseases like MERS, COVID-19, pneumonia, etc. and is considered to be one of the auspicious research areas. To analyze chest X-ray images accurately, there is a need for an expert radiologist who possesses expertise and experience in the desired domain. According to the World Health Organization (WHO) report, about 2/3 people in the world still do not have access to the radiologist, in order to diagnose their disease. This study proposes a DL framework to diagnose pneumonia disease in an efficient and effective manner. Various Deep Convolutional Neural Network (DCNN) transfer learning techniques such as AlexNet, SqueezeNet, VGG16, VGG19, and Inception-V3 are utilized for extracting useful features from the chest X-ray images. In this study, several machine learning (ML) classifiers are utilized. The proposed system has been trained and tested on chest X-ray and CT images dataset. In order to examine the stability and effectiveness of the proposed system, different performance measures have been utilized. The proposed system is intended to be beneficial and supportive for medical doctors to accurately and efficiently diagnose pneumonia disease

Deep Learning in Medical Image Analysis

The computer-assisted analysis for better interpreting images have been longstanding issues in the medical imaging field. On the image-understanding front, recent advances in machine learning, especially, in the way of deep learning, have made a big leap to help identify, classify, and quantify patterns in medical images. Specifically, exploiting hierarchical feature representations learned solely from data, instead of handcrafted features mostly designed based on domain-specific knowledge, lies at the core of the advances. In that way, deep learning is rapidly proving to be the state-of-the-art foundation, achieving enhanced performances in various medical applications. In this article, we introduce the fundamentals of deep learning methods; review their successes to image registration, anatomical/cell structures detection, tissue segmentation, computer-aided disease diagnosis or prognosis, and so on. We conclude by raising research issues and suggesting future directions for further improvements

DenResCov-19: a deep transfer learning network for robust automatic classification of COVID-19, pneumonia, and tuberculosis from X-rays

The global pandemic of coronavirus disease 2019 (COVID-19) is continuing to have a significant effect on the well-being of the global population, thus increasing the demand for rapid testing, diagnosis, and treatment. As COVID-19 can cause severe pneumonia, early diagnosis is essential for correct treatment, as well as to reduce the stress on the healthcare system. Along with COVID-19, other etiologies of pneumonia and Tuberculosis (TB) constitute additional challenges to the medical system. Pneumonia (viral as well as bacterial) kills about 2 million infants every year and is consistently estimated as one of the most important factor of childhood mortality (according to the World Health Organization). Chest X-ray (CXR) and computed tomography (CT) scans are the primary imaging modalities for diagnosing respiratory diseases. Although CT scans are the gold standard, they are more expensive, time consuming, and are associated with a small but significant dose of radiation. Hence, CXR have become more widespread as a first line investigation. In this regard, the objective of this work is to develop a new deep transfer learning pipeline, named DenResCov-19, to diagnose patients with COVID-19, pneumonia, TB or healthy based on CXR images. The pipeline consists of the existing DenseNet-121 and the ResNet-50 networks. Since the DenseNet and ResNet have orthogonal performances in some instances, in the proposed model we have created an extra layer with convolutional neural network (CNN) blocks to join these two models together to establish superior performance as compared to the two individual networks. This strategy can be applied universally in cases where two competing networks are observed. We have tested the performance of our proposed network on two-class (pneumonia and healthy), three-class (COVID-19 positive, healthy, and pneumonia), as well as four-class (COVID-19 positive, healthy, TB, and pneumonia) classification problems. We have validated that our proposed network has been able to successfully classify these lung-diseases on our four datasets and this is one of our novel findings. In particular, the AUC-ROC are 99.60, 96.51, 93.70, 96.40% and the F1 values are 98.21, 87.29, 76.09, 83.17% on our Dataset X-Ray 1, 2, 3, and 4 (DXR1, DXR2, DXR3, DXR4), respectively

Deep learning-enabled technologies for bioimage analysis.

Deep learning (DL) is a subfield of machine learning (ML), which has recently demonstrated its potency to significantly improve the quantification and classification workflows in biomedical and clinical applications. Among the end applications profoundly benefitting from DL, cellular morphology quantification is one of the pioneers. Here, we first briefly explain fundamental concepts in DL and then we review some of the emerging DL-enabled applications in cell morphology quantification in the fields of embryology, point-of-care ovulation testing, as a predictive tool for fetal heart pregnancy, cancer diagnostics via classification of cancer histology images, autosomal polycystic kidney disease, and chronic kidney diseases

Transferring kowledge to improve classification of Tuberculosis in chest X-rays

Tese de mestrado em Bioinformática e Biologia Computacional, Universidade de Lisboa, Faculdade de Ciências, 2021A Tuberculose (TB) continua a ser um dos principais problemas de saúde global na actualidade, com especial incidência em países de terceiro mundo pertencentes a África e Sudoeste Asiático, que somam 84% dos 1.5 milhões de óbitos derivados de TB durante o ano de 2017. A interpretação de Raio-X é um indicador forte no diagnóstico de TB que, quando combinado com outros indicadores como tosse, febre ou outros sintomas suspeitos, pode levar a um diagnóstico bastante preciso. A interpretação de uma imagem de Raio-X requer a competência de um Médico Radiologista experiente, um requisito limitado especialmente considerando a incidência de TB em países de terceiro mundo. Esta interpretação pode ser facilitada através do uso de Redes Neuronais Convolucionais (CNN) que, quando treinadas correctamente, conseguem ultrapassar o desempenho de profissionais de saúde. No entanto, o correcto treino de CNN requer largas quantidades de imagens classificadas, um recurso inexistente no domínio público para TB. O uso de Aprendizagem por Transferência, de fácil implementação para CNNs, é uma solução bastante popular na implementação de CNNs para a interpretação de imagens médicas, contornando os largos requisitos de imagens. Contudo, a sua comum implementação tende a não usar uma abordagem eficaz, e poucos trabalhos exploram as vantagens do uso de Aprendizagem por Transferência. Este trabalho procura explorar o uso de Aprendizagem por Transferência para a optimização do treino de CNNs em conjuntos de dados de TB bastante limitados. A exploração passa pelo uso de Bases de Referência Aleatórias e treinadas no grande conjunto de dados ImageNet, de modo a explorar as vantagens do uso de Aprendizagem por Transferência. Além destes, cinco Bases de Referência adicionais são treinadas em dois conjuntos de Raio-X de larga escala, o ChestX-ray8 e o CheXpert, na tentativa de optimizar a transferência de conhecimento para a classificação de TB. O treino de modelos em TB faz uso do conjunto de dados \Shenzhen Hospital X-ray Set", no qual os modelos são treinados, validados e testados. O conjunto de dados \Montgomery Hospital X-ray Set" é usado apenas para teste. O resultado deste trabalho são 155 classificadores de TB, para os quais os melhores resultados são atingidos usando uma Base de Referência treinada no conjunto completo de CheXpert, atingindo um valor mediano de 0.65 de WAF, e 0.77 de AUROC, no conjunto de teste externo. Adicionalmente, este trabalho verifica resultados mais optimistas pelas medidas de AUROC. Esta diferença resulta do limite usado para sumarizar o output das redes, para o qual este trabalho sugere uma estimativa alternativa usando um número limitado de dados de teste que acaba por melhorar os resultados de WAF, aproximando-os das medidas de AUROC.Tuberculosis (TB) continues to be one of the main sources of global health concern, with increased incidence in third world countries in Africa and Southwest Asia, which account for 84% of the 1.5 million deaths due to TB during the year of 2017. The interpretation of X-ray is a strong indicator in the diagnosis of TB which, when combined with other indicators such as cough, fever or other suspicious symptoms, can lead to a very accurate diagnosis. The interpretation of an X-ray image requires the expertise of an experienced Radiologist, a limited resource emphasized by the incidence of TB in third world countries. This interpretation can be assisted through the use of Convolutional Neural Networks (CNN) which, when properly trained, can surpass the performance of health professionals. However, the correct training of CNN requires large amounts of classified images, a resource that does not exist in the public domain for TB. The use of Transfer Learning is a very popular solution when implementing CNNs for the interpretation of medical images, bypassing the wide requirements of images. However, its common implementation tends to not use an effective approach, and few studies explore the advantages of using Transfer Learning. This work seeks to explore the use of Transfer Learning for the optimization of CNN training in very limited TB datasets. Exploration involves the use of Random Baselines and Baselines trained on the large dataset ImageNet, exploring the advantages of Transfer Learning. In addition to these, five additional Baselines are trained on two large-scale X-ray sets, the ChestX-ray8 and the CheXpert, in an attempt to optimize the transfer of knowledge for the classification of TB. The training of models for TB uses the \Shenzhen Hospital X-ray Set" dataset for training, validation and testing. The \Montgomery Hospital X-ray Set" dataset is used for testing purposes only. The result of this work is 155 TB classifiers, for which the best results are achieved using a Baseline trained in the complete set of CheXpert, reaching a median value of 0.65 WAF, and 0.77 of AUROC, on the external test set. Additionally, this work verifies more optimistic results for AUROC measures. This difference results from the threshold used to summarize the output of the networks, for which this work suggests an alternative estimate using a limited number of test data that ends up improving the results of WAF, bringing them closer to the AUROC measures