Cancer diagnosis using deep learning: A bibliographic review

In this paper, we first describe the basics of the field of cancer diagnosis, which includes steps of cancer diagnosis followed by the typical classification methods used by doctors, providing a historical idea of cancer classification techniques to the readers. These methods include Asymmetry, Border, Color and Diameter (ABCD) method, seven-point detection method, Menzies method, and pattern analysis. They are used regularly by doctors for cancer diagnosis, although they are not considered very efficient for obtaining better performance. Moreover, considering all types of audience, the basic evaluation criteria are also discussed. The criteria include the receiver operating characteristic curve (ROC curve), Area under the ROC curve (AUC), F1 score, accuracy, specificity, sensitivity, precision, dice-coefficient, average accuracy, and Jaccard index. Previously used methods are considered inefficient, asking for better and smarter methods for cancer diagnosis. Artificial intelligence and cancer diagnosis are gaining attention as a way to define better diagnostic tools. In particular, deep neural networks can be successfully used for intelligent image analysis. The basic framework of how this machine learning works on medical imaging is provided in this study, i.e., pre-processing, image segmentation and post-processing. The second part of this manuscript describes the different deep learning techniques, such as convolutional neural networks (CNNs), generative adversarial models (GANs), deep autoencoders (DANs), restricted Boltzmann’s machine (RBM), stacked autoencoders (SAE), convolutional autoencoders (CAE), recurrent neural networks (RNNs), long short-term memory (LTSM), multi-scale convolutional neural network (M-CNN), multi-instance learning convolutional neural network (MIL-CNN). For each technique, we provide Python codes, to allow interested readers to experiment with the cited algorithms on their own diagnostic problems. The third part of this manuscript compiles the successfully applied deep learning models for different types of cancers. Considering the length of the manuscript, we restrict ourselves to the discussion of breast cancer, lung cancer, brain cancer, and skin cancer. The purpose of this bibliographic review is to provide researchers opting to work in implementing deep learning and artificial neural networks for cancer diagnosis a knowledge from scratch of the state-of-the-art achievements

Classificação de nódulos pulmonares baseada em redes neurais convolucionais profundas em radiografias

Orientador: Hélio PedriniDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: O câncer de pulmão, que se caracteriza pela presença de nódulos, é o tipo mais comum de câncer em todo o mundo, além de ser um dos mais agressivos e fatais, com 20% da mortalidade total por câncer. A triagem do câncer de pulmão pode ser realizada por radiologistas que analisam imagens de raios-X de tórax (CXR). No entanto, a detecção de nódulos pulmonares é uma tarefa difícil devido a sua grande variabilidade, limitações humanas de memória, distração e fadiga, entre outros fatores. Essas dificuldades motivam o desenvolvimento de sistemas de diagnóstico por computador (CAD) para apoiar radiologistas na detecção de nódulos pulmonares. A classificação do nódulo do pulmão é um dos principais tópicos relacionados aos sistemas de CAD. Embora as redes neurais convolucionais (CNN) tenham demonstrado um bom desempenho em muitas tarefas, há poucas explorações de seu uso para classificar nódulos pulmonares em imagens CXR. Neste trabalho, propusemos e analisamos um arcabouço para a detecção de nódulos pulmonares em imagens de CXR que inclui segmentação da área pulmonar, localização de nódulos e classificação de nódulos candidatos. Apresentamos um método para classificação de nódulos candidatos com CNNs treinadas a partir do zero. A eficácia do nosso método baseia-se na seleção de parâmetros de aumento de dados, no projeto de uma arquitetura CNN especializada, no uso da regularização de dropout na rede, inclusive em camadas convolucionais, e no tratamento da falta de amostras de nódulos em comparação com amostras de fundo, balanceando mini-lotes em cada iteração da descida do gradiente estocástico. Todas as decisões de seleção do modelo foram tomadas usando-se um subconjunto de imagens CXR da base Lung Image Database Consortium and Image Database Resource Initiative (LIDC/IDRI) separadamente. Então, utilizamos todas as imagens com nódulos no conjunto de dados da Japanese Society of Radiological Technology (JSRT) para avaliação. Nossos experimentos mostraram que as CNNs foram capazes de alcançar resultados competitivos quando comparados com métodos da literatura. Nossa proposta obteve uma curva de operação (AUC) de 7.51 considerando 10 falsos positivos por imagem (FPPI) e uma sensibilidade de 71.4% e 81.0% com 2 e 5 FPPI, respectivamenteAbstract: Lung cancer, which is characterized by the presence of nodules, is the most common type of cancer around the world, as well as one of the most aggressive and deadliest cancer, with 20% of total cancer mortality. Lung cancer screening can be performed by radiologists analyzing chest X-ray (CXR) images. However, the detection of lung nodules is a difficult task due to their wide variability, human limitations of memory, distraction and fatigue, among other factors. These difficulties motivate the development of computer-aided diagnosis (CAD) systems for supporting radiologists in detecting lung nodules. Lung nodule classification is one of the main topics related to CAD systems. Although convolutional neural networks (CNN) have been demonstrated to perform well on many tasks, there are few explorations of their use for classifying lung nodules in CXR images. In this work, we proposed and analyzed a pipeline for detecting lung nodules in CXR images that includes lung area segmentation, potential nodule localization, and nodule candidate classification. We presented a method for classifying nodule candidates with a CNN trained from the scratch. The effectiveness of our method relies on the selection of data augmentation parameters, the design of a specialized CNN architecture, the use of dropout regularization on the network, inclusive in convolutional layers, and addressing the lack of nodule samples compared to background samples balancing mini-batches on each stochastic gradient descent iteration. All model selection decisions were taken using a CXR subset of the Lung Image Database Consortium and Image Database Resource Initiative (LIDC/IDRI) dataset separately. Thus, we used all images with nodules in the Japanese Society of Radiological Technology (JSRT) dataset for evaluation. Our experiments showed that CNNs were capable of achieving competitive results when compared to state-of-the-art methods. Our proposal obtained an area under the free-response receiver operating characteristic (AUC) curve of 7.51 considering 10 false positives per image (FPPI), and a sensitivity of 71.4% and 81.0% with 2 and 5 FPPI, respectivelyMestradoCiência da ComputaçãoMestre em Ciência da ComputaçãoCAPE

Artificial intelligence in cancer imaging: Clinical challenges and applications

Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered data with nuanced decision making. Cancer offers a unique context for medical decisions given not only its variegated forms with evolution of disease but also the need to take into account the individual condition of patients, their ability to receive treatment, and their responses to treatment. Challenges remain in the accurate detection, characterization, and monitoring of cancers despite improved technologies. Radiographic assessment of disease most commonly relies upon visual evaluations, the interpretations of which may be augmented by advanced computational analyses. In particular, artificial intelligence (AI) promises to make great strides in the qualitative interpretation of cancer imaging by expert clinicians, including volumetric delineation of tumors over time, extrapolation of the tumor genotype and biological course from its radiographic phenotype, prediction of clinical outcome, and assessment of the impact of disease and treatment on adjacent organs. AI may automate processes in the initial interpretation of images and shift the clinical workflow of radiographic detection, management decisions on whether or not to administer an intervention, and subsequent observation to a yet to be envisioned paradigm. Here, the authors review the current state of AI as applied to medical imaging of cancer and describe advances in 4 tumor types (lung, brain, breast, and prostate) to illustrate how common clinical problems are being addressed. Although most studies evaluating AI applications in oncology to date have not been vigorously validated for reproducibility and generalizability, the results do highlight increasingly concerted efforts in pushing AI technology to clinical use and to impact future directions in cancer care

Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases

Cardiothoracic and pulmonary diseases are a significant cause of mortality and morbidity worldwide. The COVID-19 pandemic has highlighted the lack of access to clinical care, the overburdened medical system, and the potential of artificial intelligence (AI) in improving medicine. There are a variety of diseases affecting the cardiopulmonary system including lung cancers, heart disease, tuberculosis (TB), etc., in addition to COVID-19-related diseases. Screening, diagnosis, and management of cardiopulmonary diseases has become difficult owing to the limited availability of diagnostic tools and experts, particularly in resource-limited regions. Early screening, accurate diagnosis and staging of these diseases could play a crucial role in treatment and care, and potentially aid in reducing mortality. Radiographic imaging methods such as computed tomography (CT), chest X-rays (CXRs), and echo ultrasound (US) are widely used in screening and diagnosis. Research on using image-based AI and machine learning (ML) methods can help in rapid assessment, serve as surrogates for expert assessment, and reduce variability in human performance. In this Special Issue, “Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases”, we have highlighted exemplary primary research studies and literature reviews focusing on novel AI/ML methods and their application in image-based screening, diagnosis, and clinical management of cardiopulmonary diseases. We hope that these articles will help establish the advancements in AI

3D Multimodal Brain Tumor Segmentation and Grading Scheme based on Machine, Deep, and Transfer Learning Approaches

Glioma is one of the most common tumors of the brain. The detection and grading of glioma at an early stage is very critical for increasing the survival rate of the patients. Computer-aided detection (CADe) and computer-aided diagnosis (CADx) systems are essential and important tools that provide more accurate and systematic results to speed up the decision-making process of clinicians. In this paper, we introduce a method consisting of the variations of the machine, deep, and transfer learning approaches for the effective brain tumor (i.e., glioma) segmentation and grading on the multimodal brain tumor segmentation (BRATS) 2020 dataset. We apply popular and efficient 3D U-Net architecture for the brain tumor segmentation phase. We also utilize 23 different combinations of deep feature sets and machine learning/fine-tuned deep learning CNN models based on Xception, IncResNetv2, and EfficientNet by using 4 different feature sets and 6 learning models for the tumor grading phase. The experimental results demonstrate that the proposed method achieves 99.5% accuracy rate for slice-based tumor grading on BraTS 2020 dataset. Moreover, our method is found to have competitive performance with similar recent works

Women in Artificial intelligence (AI)

This Special Issue, entitled "Women in Artificial Intelligence" includes 17 papers from leading women scientists. The papers cover a broad scope of research areas within Artificial Intelligence, including machine learning, perception, reasoning or planning, among others. The papers have applications to relevant fields, such as human health, finance, or education. It is worth noting that the Issue includes three papers that deal with different aspects of gender bias in Artificial Intelligence. All the papers have a woman as the first author. We can proudly say that these women are from countries worldwide, such as France, Czech Republic, United Kingdom, Australia, Bangladesh, Yemen, Romania, India, Cuba, Bangladesh and Spain. In conclusion, apart from its intrinsic scientific value as a Special Issue, combining interesting research works, this Special Issue intends to increase the invisibility of women in AI, showing where they are, what they do, and how they contribute to developments in Artificial Intelligence from their different places, positions, research branches and application fields. We planned to issue this book on the on Ada Lovelace Day (11/10/2022), a date internationally dedicated to the first computer programmer, a woman who had to fight the gender difficulties of her times, in the XIX century. We also thank the publisher for making this possible, thus allowing for this book to become a part of the international activities dedicated to celebrating the value of women in ICT all over the world. With this book, we want to pay homage to all the women that contributed over the years to the field of AI

Deep Learning in Medical Image Analysis

The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis