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

Intelligent Biosignal Processing in Wearable and Implantable Sensors

This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine

Machine Learning towards General Medical Image Segmentation

The quality of patient care associated with diagnostic radiology is proportionate to a physician\u27s workload. Segmentation is a fundamental limiting precursor to diagnostic and therapeutic procedures. Advances in machine learning aims to increase diagnostic efficiency to replace single applications with generalized algorithms. We approached segmentation as a multitask shape regression problem, simultaneously predicting coordinates on an object\u27s contour while jointly capturing global shape information. Shape regression models inherent point correlations to recover ambiguous boundaries not supported by clear edges and region homogeneity. Its capabilities was investigated using multi-output support vector regression (MSVR) on head and neck (HaN) CT images. Subsequently, we incorporated multiplane and multimodality spinal images and presented the first deep learning multiapplication framework for shape regression, the holistic multitask regression network (HMR-Net). MSVR and HMR-Net\u27s performance were comparable or superior to state-of-the-art algorithms. Multiapplication frameworks bridges any technical knowledge gaps and increases workflow efficiency

Learning Disentangled Representations in the Imaging Domain

Disentangled representation learning has been proposed as an approach to learning general representations even in the absence of, or with limited, supervision. A good general representation can be fine-tuned for new target tasks using modest amounts of data, or used directly in unseen domains achieving remarkable performance in the corresponding task. This alleviation of the data and annotation requirements offers tantalising prospects for applications in computer vision and healthcare. In this tutorial paper, we motivate the need for disentangled representations, present key theory, and detail practical building blocks and criteria for learning such representations. We discuss applications in medical imaging and computer vision emphasising choices made in exemplar key works. We conclude by presenting remaining challenges and opportunities.Comment: Submitted. This paper follows a tutorial style but also surveys a considerable (more than 200 citations) number of work

Learning Biosignals with Deep Learning

The healthcare system, which is ubiquitously recognized as one of the most influential system in society, is facing new challenges since the start of the decade.The myriad of physiological data generated by individuals, namely in the healthcare system, is generating a burden on physicians, losing effectiveness on the collection of patient data. Information systems and, in particular, novel deep learning (DL) algorithms have been prompting a way to take this problem. This thesis has the aim to have an impact in biosignal research and industry by presenting DL solutions that could empower this field. For this purpose an extensive study of how to incorporate and implement Convolutional Neural Networks (CNN), Recursive Neural Networks (RNN) and Fully Connected Networks in biosignal studies is discussed. Different architecture configurations were explored for signal processing and decision making and were implemented in three different scenarios: (1) Biosignal learning and synthesis; (2) Electrocardiogram (ECG) biometric systems, and; (3) Electrocardiogram (ECG) anomaly detection systems. In (1) a RNN-based architecture was able to replicate autonomously three types of biosignals with a high degree of confidence. As for (2) three CNN-based architectures, and a RNN-based architecture (same used in (1)) were used for both biometric identification, reaching values above 90% for electrode-base datasets (Fantasia, ECG-ID and MIT-BIH) and 75% for off-person dataset (CYBHi), and biometric authentication, achieving Equal Error Rates (EER) of near 0% for Fantasia and MIT-BIH and bellow 4% for CYBHi. As for (3) the abstraction of healthy clean the ECG signal and detection of its deviation was made and tested in two different scenarios: presence of noise using autoencoder and fully-connected network (reaching 99% accuracy for binary classification and 71% for multi-class), and; arrhythmia events by including a RNN to the previous architecture (57% accuracy and 61% sensitivity). In sum, these systems are shown to be capable of producing novel results. The incorporation of several AI systems into one could provide to be the next generation of preventive medicine, as the machines have access to different physiological and anatomical states, it could produce more informed solutions for the issues that one may face in the future increasing the performance of autonomous preventing systems that could be used in every-day life in remote places where the access to medicine is limited. These systems will also help the study of the signal behaviour and how they are made in real life context as explainable AI could trigger this perception and link the inner states of a network with the biological traits.O sistema de saúde, que é ubiquamente reconhecido como um dos sistemas mais influentes da sociedade, enfrenta novos desafios desde o ínicio da década. A miríade de dados fisiológicos gerados por indíviduos, nomeadamente no sistema de saúde, está a gerar um fardo para os médicos, perdendo a eficiência no conjunto dos dados do paciente. Os sistemas de informação e, mais espcificamente, da inovação de algoritmos de aprendizagem profunda (DL) têm sido usados na procura de uma solução para este problema. Esta tese tem o objetivo de ter um impacto na pesquisa e na indústria de biosinais, apresentando soluções de DL que poderiam melhorar esta área de investigação. Para esse fim, é discutido um extenso estudo de como incorporar e implementar redes neurais convolucionais (CNN), redes neurais recursivas (RNN) e redes totalmente conectadas para o estudo de biosinais. Diferentes arquiteturas foram exploradas para processamento e tomada de decisão de sinais e foram implementadas em três cenários diferentes: (1) Aprendizagem e síntese de biosinais; (2) sistemas biométricos com o uso de eletrocardiograma (ECG), e; (3) Sistema de detecção de anomalias no ECG. Em (1) uma arquitetura baseada na RNN foi capaz de replicar autonomamente três tipos de sinais biológicos com um alto grau de confiança. Quanto a (2) três arquiteturas baseadas em CNN e uma arquitetura baseada em RNN (a mesma usada em (1)) foram usadas para ambas as identificações, atingindo valores acima de 90 % para conjuntos de dados à base de eletrodos (Fantasia, ECG-ID e MIT -BIH) e 75 % para o conjunto de dados fora da pessoa (CYBHi) e autenticação, atingindo taxas de erro iguais (EER) de quase 0 % para Fantasia e MIT-BIH e abaixo de 4 % para CYBHi. Quanto a (3) a abstração de sinais limpos e assimptomáticos de ECG e a detecção do seu desvio foram feitas e testadas em dois cenários diferentes: na presença de ruído usando um autocodificador e uma rede totalmente conectada (atingindo 99 % de precisão na classificação binária e 71 % na multi-classe), e; eventos de arritmia incluindo um RNN na arquitetura anterior (57 % de precisão e 61 % de sensibilidade). Em suma, esses sistemas são mais uma vez demonstrados como capazes de produzir resultados inovadores. A incorporação de vários sistemas de inteligência artificial em um unico sistema pederá desencadear a próxima geração de medicina preventiva. Os algoritmos ao terem acesso a diferentes estados fisiológicos e anatómicos, podem produzir soluções mais informadas para os problemas que se possam enfrentar no futuro, aumentando o desempenho de sistemas autónomos de prevenção que poderiam ser usados na vida quotidiana, nomeadamente em locais remotos onde o acesso à medicinas é limitado. Estes sistemas também ajudarão o estudo do comportamento do sinal e como eles são feitos no contexto da vida real, pois a IA explicável pode desencadear essa percepção e vincular os estados internos de uma rede às características biológicas

Optimization of neural networks for deep learning and applications to CT image segmentation

[eng] During the last few years, AI development in deep learning has been going so fast that even important researchers, politicians, and entrepreneurs are signing petitions to try to slow it down. The newest methods for natural language processing and image generation are achieving results so unbelievable that people are seriously starting to think they can be dangerous for society. In reality, they are not dangerous (at the moment) even if we have to admit we reached a point where we have no more control over the flux of data inside the deep networks. It is impossible to open a modern deep neural network and interpret how it processes the information and, in many cases, explain how or why it gives back that particular result. One of the goals of this doctoral work has been to study the behavior of weights in convolutional neural networks and in transformers. We hereby present a work that demonstrates how to invert 3x3 convolutions after training a neural network able to learn how to classify images, with the future aim of having precisely invertible convolutional neural networks. We demonstrate that a simple network can learn to classify images on an open-source dataset without loss in accuracy, with respect to a non-invertible one. All that with the ability to reconstruct the original image without detectable error (on 8-bit images) in up to 20 convolutions stacked in a row. We present a thorough comparison between our method and the standard. We tested the performances of the five most used transformers for image classification on an open- source dataset. Studying the embedded matrices, we have been able to provide two criteria that can help transformers learn with a training time reduction of up to 30% and with no impact on classification accuracy. The evolution of deep learning techniques is also touching the field of digital health. With tens of thousands of new start-ups and more than 1B $ of investments only in the last year, this field is growing rapidly and promising to revolutionize healthcare. In this thesis, we present several neural networks for the segmentation of lungs, lung nodules, and areas affected by pneumonia induced by COVID-19, in chest CT scans. The architecturesm we used are all residual convolutional neural networks inspired by UNet and Inception. We customized them with novel loss functions and layers studied to achieve high performances on these particular applications. The errors on the surface of nodule segmentation masks are not over 1mm in more than 99% of the cases. Our algorithm for COVID-19 lesion detection has a specificity of 100% and overall accuracy of 97.1%. In general, it surpasses the state-of-the-art in all the considered statistics, using UNet as a benchmark. Combining these with other algorithms able to detect and predict lung cancer, the whole work was presented in a European innovation program and judged of high interest by worldwide experts. With this work, we set the basis for the future development of better AI tools in healthcare and scientific investigation into the fundamentals of deep learning.[spa] Durante los últimos años, el desarrollo de la IA en el aprendizaje profundo ha ido tan rápido que Incluso importantes investigadores, políticos y empresarios están firmando peticiones para intentar para ralentizarlo. Los métodos más nuevos para el procesamiento y la generación de imágenes y lenguaje natural, están logrando resultados tan increíbles que la gente está empezando a preocuparse seriamente. Pienso que pueden ser peligrosos para la sociedad. En realidad, no son peligrosos (al menos de momento) incluso si tenemos que admitir que llegamos a un punto en el que ya no tenemos control sobre el flujo de datos dentro de las redes profundas. Es imposible abrir una moderna red neuronal profunda e interpretar cómo procesa la información y, en muchos casos, explique cómo o por qué devuelve ese resultado en particular, uno de los objetivos de este doctorado. El trabajo ha consistido en estudiar el comportamiento de los pesos en redes neuronales convolucionales y en transformadores. Por la presente presentamos un trabajo que demuestra cómo invertir 3x3 convoluciones después de entrenar una red neuronal capaz de aprender a clasificar imágenes, con el objetivo futuro de tener redes neuronales convolucionales precisamente invertibles. Nosotros queremos demostrar que una red simple puede aprender a clasificar imágenes en un código abierto conjunto de datos sin pérdida de precisión, con respecto a uno no invertible. Todo eso con la capacidad de reconstruir la imagen original sin errores detectables (en imágenes de 8 bits) en hasta 20 convoluciones apiladas en fila. Presentamos una exhaustiva comparación entre nuestro método y el estándar. Probamos las prestaciones de los cinco transformadores más utilizados para la clasificación de imágenes en abierto. conjunto de datos de origen. Al estudiar las matrices incrustadas, hemos sido capaz de proporcionar dos criterios que pueden ayudar a los transformadores a aprender con un tiempo de capacitación reducción de hasta el 30% y sin impacto en la precisión de la clasificación. La evolución de las técnicas de aprendizaje profundo también está afectando al campo de la salud digital. Con decenas de miles de nuevas empresas y más de mil millones de dólares en inversiones sólo en el año pasado, este campo está creciendo rápidamente y promete revolucionar la atención médica. En esta tesis, presentamos varias redes neuronales para la segmentación de pulmones, nódulos pulmonares, y zonas afectadas por neumonía inducida por COVID-19, en tomografías computarizadas de tórax. La arquitectura que utilizamos son todas redes neuronales convolucionales residuales inspiradas en UNet. Las personalizamos con nuevas funciones y capas de pérdida, estudiado para lograr altos rendimientos en estas aplicaciones particulares. Los errores en la superficie de las máscaras de segmentación de los nódulos no supera 1 mm en más del 99% de los casos. Nuestro algoritmo para la detección de lesiones de COVID-19 tiene una especificidad del 100% y en general precisión del 97,1%. En general supera el estado del arte en todos los aspectos considerados, estadísticas, utilizando UNet como punto de referencia. Combinando estos con otros algoritmos capaces de detectar y predecir el cáncer de pulmón, todo el trabajo se presentó en una innovación europea programa y considerado de gran interés por expertos de todo el mundo. Con este trabajo, sentamos las bases para el futuro desarrollo de mejores herramientas de IA en Investigación sanitaria y científica sobre los fundamentos del aprendizaje profundo

Classification of patients with parkinsonian syndromes using medical imaging and artificial intelligence algorithms

The distinction of Parkinsonian Syndromes (PS) is challenging due to similarities of symptoms and signs at early stages of disease. Thus, the need of accurate methods for differential diagnosis at those early stages has emerged. To improve the evaluation of medical images, artificial intelligence turns out to be a useful tool. Parkinson’s Disease, the commonest PS, is characterized by the degeneration of dopamine neurons in the substantia nigra which is detected by the dopamine transporter scan (DaTscanTM), a single photon-emission tomography (SPECT) exam that uses of a radiotracer that binds dopamine receptors. In fact, by using such exam it was possible to identify a sub-group of PD patients known as “Scans without evidence of dopaminergic deficit” (SWEDD) that present a normal exam, unlike PD patients. In this study, an approach based on Convolutional Neural Networks (CNNs) was proposed for classifying PD patients, SWEDD patients and healthy subjects using SPECT and Magnetic Resonance Imaging (MRI) images. Then, these images were divided into subsets of slices in the axial view that contains particular regions of interest since 2D images are the norm in clinical practice. The classifier evaluation was performed with Cohen’s Kappa and Receiver Operating Characteristic (ROC) curve. The results obtained allow to conclude that the CNN using imaging information of the Basal Ganglia and the mesencephalon was able to distinguish PD patients from healthy subjects since achieved 97.4% accuracy using MRI and 92.4% accuracy using SPECT, and PD from SWEDD with 97.3% accuracy using MRI and 93.3% accuracy using SPECT. Nonetheless, using the same approach, it was not possible to discriminate SWEDD patients from healthy subjects (60% accuracy) using DaTscanTM and MRI. These results allow to conclude that this approach may be a useful tool to aid in PD diagnosis in the future