Modeling Camera Effects to Improve Visual Learning from Synthetic Data

Recent work has focused on generating synthetic imagery to increase the size and variability of training data for learning visual tasks in urban scenes. This includes increasing the occurrence of occlusions or varying environmental and weather effects. However, few have addressed modeling variation in the sensor domain. Sensor effects can degrade real images, limiting generalizability of network performance on visual tasks trained on synthetic data and tested in real environments. This paper proposes an efficient, automatic, physically-based augmentation pipeline to vary sensor effects --chromatic aberration, blur, exposure, noise, and color cast-- for synthetic imagery. In particular, this paper illustrates that augmenting synthetic training datasets with the proposed pipeline reduces the domain gap between synthetic and real domains for the task of object detection in urban driving scenes

Combining Synthesis of Cardiorespiratory Signals and Artifacts with Deep Learning for Robust Vital Sign Estimation

Healthcare has been remarkably morphing on the account of Big Data. As Machine Learning (ML) consolidates its place in simpler clinical chores, more complex Deep Learning (DL) algorithms have struggled to keep up, despite their superior capabilities. This is mainly attributed to the need for large amounts of data for training, which the scientific community is unable to satisfy. The number of promising DL algorithms is considerable, although solutions directly targeting the shortage of data lack. Currently, dynamical generative models are the best bet, but focus on single, classical modalities and tend to complicate significantly with the amount of physiological effects they can simulate. This thesis aims at providing and validating a framework, specifically addressing the data deficit in the scope of cardiorespiratory signals. Firstly, a multimodal statistical synthesizer was designed to generate large, annotated artificial signals. By expressing data through coefficients of pre-defined, fitted functions and describing their dependence with Gaussian copulas, inter- and intra-modality associations were learned. Thereafter, new coefficients are sampled to generate artificial, multimodal signals with the original physiological dynamics. Moreover, normal and pathological beats along with artifacts were included by employing Markov models. Secondly, a convolutional neural network (CNN) was conceived with a novel sensor-fusion architecture and trained with synthesized data under real-world experimental conditions to evaluate how its performance is affected. Both the synthesizer and the CNN not only performed at state of the art level but also innovated with multiple types of generated data and detection error improvements, respectively. Cardiorespiratory data augmentation corrected performance drops when not enough data is available, enhanced the CNN’s ability to perform on noisy signals and to carry out new tasks when introduced to, otherwise unavailable, types of data. Ultimately, the framework was successfully validated showing potential to leverage future DL research on Cardiology into clinical standards

DPDnet: A Robust People Detector using Deep Learning with an Overhead Depth Camera

In this paper we propose a method based on deep learning that detects multiple people from a single overhead depth image with high reliability. Our neural network, called DPDnet, is based on two fully-convolutional encoder-decoder neural blocks based on residual layers. The Main Block takes a depth image as input and generates a pixel-wise confidence map, where each detected person in the image is represented by a Gaussian-like distribution. The refinement block combines the depth image and the output from the main block, to refine the confidence map. Both blocks are simultaneously trained end-to-end using depth images and head position labels. The experimental work shows that DPDNet outperforms state-of-the-art methods, with accuracies greater than 99% in three different publicly available datasets, without retraining not fine-tuning. In addition, the computational complexity of our proposal is independent of the number of people in the scene and runs in real time using conventional GPUs

Artificial Intelligence in Radiation Therapy

Artificial intelligence (AI) has great potential to transform the clinical workflow of radiotherapy. Since the introduction of deep neural networks, many AI-based methods have been proposed to address challenges in different aspects of radiotherapy. Commercial vendors have started to release AI-based tools that can be readily integrated to the established clinical workflow. To show the recent progress in AI-aided radiotherapy, we have reviewed AI-based studies in five major aspects of radiotherapy including image reconstruction, image registration, image segmentation, image synthesis, and automatic treatment planning. In each section, we summarized and categorized the recently published methods, followed by a discussion of the challenges, concerns, and future development. Given the rapid development of AI-aided radiotherapy, the efficiency and effectiveness of radiotherapy in the future could be substantially improved through intelligent automation of various aspects of radiotherapy

Low-cost portable microscopy systems for biomedical imaging and healthcare applications

In recent years, the development of low-cost portable microscopes (LPMs) has opened new possibilities for disease detection and biomedical research, especially in resource-limited areas. Despite these advancements, the majority of existing LPMs are hampered by sophisticated optical and mechanical designs, require extensive post-data analysis, and are often tailored for specific biomedical applications, limiting their broader utility. Furthermore, creating an optical-sectioning microscope that is both compact and cost effective presents a significant challenge. Addressing these critical gaps, this PhD study aims to: (1) develop a universally applicable LPM featuring a simplified mechanical and optical design for real-time biomedical imaging analysis, and (2) design a novel, smartphone-based optical sectioning microscope that is both compact and affordable. These objectives are driven by the need to enhance accessibility to quality diagnostic tools in varied settings, promising a significant leap forward in the democratization of biomedical imaging technologies. With 3D printing, optimised optical design, and AI techniques, we can develop LPM’s real time analysis functionality. I conducted a literature review on LPMs and related applications in my study and implemented two low-cost prototype microscopes and one theoretical study. 1) The first project is a portable AI fluorescence microscope based on a webcam and the NVIDIA Jetson Nano (NJN) with real-time analysis functionality. The system was 3D printed, weighing ~250 grams with a size of 145mm × 172 mm × 144 mm (L×W×H) and costing ~$400. It achieves a physical magnification of ×5 and can resolve 228.1 lp/mm USAF features. The system can recognise and count fluorescent beads and human red blood cells (RBCs). 2) I developed a smartphone-based optical sectioning microscope using the HiLo technique. To our knowledge, it is the first smartphone-based HiLo microscope that offers low-cost optical-sectioned widefield imaging. It has a 571.5 μm telecentric scanning range and an 11.7 μm axial resolution. I successfully used it to realize optical sectioning imaging of fluorescent beads. For this system, I developed a new low-cost HiLo microscopy technique using microlens arrays (MLAs) with incoherent light-emitting diode (LED) light sources. I conducted a numerical simulation study assessing the integration of uncoherent LEDs and MLAs for a low-cost HiLo system. The MLA can generate structured illumination in HiLo. How the MLA’s geometry structure and physical parameters affect the image performance were discussed in detail. This PhD thesis explores the advancement of low-cost portable microscopes (LPMs) through the integration of 3D printing, optimized optical design, and artificial intelligence (AI) techniques to enhance their real-time analysis capabilities. The research involved a comprehensive literature review on LPMs and their applications, leading to the development of two innovative prototype LPMs, alongside a theoretical study. These works contribute significantly to the field by not only addressing the technical and financial barriers associated with advanced microscopy but also by laying the groundwork for future innovations in portable and accessible biomedical imaging. Through its focus on simplification, affordability, and practicality, the research holds promise for substantially expanding the reach and impact of diagnostic imaging technologies, especially in those resource-limited areas.In recent years, the development of low-cost portable microscopes (LPMs) has opened new possibilities for disease detection and biomedical research, especially in resource-limited areas. Despite these advancements, the majority of existing LPMs are hampered by sophisticated optical and mechanical designs, require extensive post-data analysis, and are often tailored for specific biomedical applications, limiting their broader utility. Furthermore, creating an optical-sectioning microscope that is both compact and cost effective presents a significant challenge. Addressing these critical gaps, this PhD study aims to: (1) develop a universally applicable LPM featuring a simplified mechanical and optical design for real-time biomedical imaging analysis, and (2) design a novel, smartphone-based optical sectioning microscope that is both compact and affordable. These objectives are driven by the need to enhance accessibility to quality diagnostic tools in varied settings, promising a significant leap forward in the democratization of biomedical imaging technologies. With 3D printing, optimised optical design, and AI techniques, we can develop LPM’s real time analysis functionality. I conducted a literature review on LPMs and related applications in my study and implemented two low-cost prototype microscopes and one theoretical study. 1) The first project is a portable AI fluorescence microscope based on a webcam and the NVIDIA Jetson Nano (NJN) with real-time analysis functionality. The system was 3D printed, weighing ~250 grams with a size of 145mm × 172 mm × 144 mm (L×W×H) and costing ~$400. It achieves a physical magnification of ×5 and can resolve 228.1 lp/mm USAF features. The system can recognise and count fluorescent beads and human red blood cells (RBCs). 2) I developed a smartphone-based optical sectioning microscope using the HiLo technique. To our knowledge, it is the first smartphone-based HiLo microscope that offers low-cost optical-sectioned widefield imaging. It has a 571.5 μm telecentric scanning range and an 11.7 μm axial resolution. I successfully used it to realize optical sectioning imaging of fluorescent beads. For this system, I developed a new low-cost HiLo microscopy technique using microlens arrays (MLAs) with incoherent light-emitting diode (LED) light sources. I conducted a numerical simulation study assessing the integration of uncoherent LEDs and MLAs for a low-cost HiLo system. The MLA can generate structured illumination in HiLo. How the MLA’s geometry structure and physical parameters affect the image performance were discussed in detail. This PhD thesis explores the advancement of low-cost portable microscopes (LPMs) through the integration of 3D printing, optimized optical design, and artificial intelligence (AI) techniques to enhance their real-time analysis capabilities. The research involved a comprehensive literature review on LPMs and their applications, leading to the development of two innovative prototype LPMs, alongside a theoretical study. These works contribute significantly to the field by not only addressing the technical and financial barriers associated with advanced microscopy but also by laying the groundwork for future innovations in portable and accessible biomedical imaging. Through its focus on simplification, affordability, and practicality, the research holds promise for substantially expanding the reach and impact of diagnostic imaging technologies, especially in those resource-limited areas

Flexible Time Series Matching for Clinical and Behavioral Data

Time Series data became broadly applied by the research community in the last decades after a massive explosion of its availability. Nonetheless, this rise required an improvement in the existing analysis techniques which, in the medical domain, would help specialists to evaluate their patients condition. One of the key tasks in time series analysis is pattern recognition (segmentation and classification). Traditional methods typically perform subsequence matching, making use of a pattern template and a similarity metric to search for similar sequences throughout time series. However, real-world data is noisy and variable (morphological distortions), making a template-based exact matching an elementary approach. Intending to increase flexibility and generalize the pattern searching tasks across domains, this dissertation proposes two Deep Learning-based frameworks to solve pattern segmentation and anomaly detection problems. Regarding pattern segmentation, a Convolution/Deconvolution Neural Network is proposed, learning to distinguish, point-by-point, desired sub-patterns from background content within a time series. The proposed framework was validated in two use-cases: electrocardiogram (ECG) and inertial sensor-based human activity (IMU) signals. It outperformed two conventional matching techniques, being capable of notably detecting the targeted cycles even in noise-corrupted or extremely distorted signals, without using any reference template nor hand-coded similarity scores. Concerning anomaly detection, the proposed unsupervised framework uses the reconstruction ability of Variational Autoencoders and a local similarity score to identify non-labeled abnormalities. The proposal was validated in two public ECG datasets (MITBIH Arrhythmia and ECG5000), performing cardiac arrhythmia identification. Results indicated competitiveness relative to recent techniques, achieving detection AUC scores of 98.84% (ECG5000) and 93.32% (MIT-BIH Arrhythmia).Dados de séries temporais tornaram-se largamente aplicados pela comunidade científica nas últimas decadas após um aumento massivo da sua disponibilidade. Contudo, este aumento exigiu uma melhoria das atuais técnicas de análise que, no domínio clínico, auxiliaria os especialistas na avaliação da condição dos seus pacientes. Um dos principais tipos de análise em séries temporais é o reconhecimento de padrões (segmentação e classificação). Métodos tradicionais assentam, tipicamente, em técnicas de correspondência em subsequências, fazendo uso de um padrão de referência e uma métrica de similaridade para procurar por subsequências similares ao longo de séries temporais. Todavia, dados do mundo real são ruidosos e variáveis (morfologicamente), tornando uma correspondência exata baseada num padrão de referência uma abordagem rudimentar. Pretendendo aumentar a flexibilidade da análise de séries temporais e generalizar tarefas de procura de padrões entre domínios, esta dissertação propõe duas abordagens baseadas em Deep Learning para solucionar problemas de segmentação de padrões e deteção de anomalias. Acerca da segmentação de padrões, a rede neuronal de Convolução/Deconvolução proposta aprende a distinguir, ponto a ponto, sub-padrões pretendidos de conteúdo de fundo numa série temporal. O modelo proposto foi validado em dois casos de uso: sinais eletrocardiográficos (ECG) e de sensores inerciais em atividade humana (IMU). Este superou duas técnicas convencionais, sendo capaz de detetar os ciclos-alvo notavelmente, mesmo em sinais corrompidos por ruído ou extremamente distorcidos, sem o uso de nenhum padrão de referência nem métricas de similaridade codificadas manualmente. A respeito da deteção de anomalias, a técnica não supervisionada proposta usa a capacidade de reconstrução dos Variational Autoencoders e uma métrica de similaridade local para identificar anomalias desconhecidas. A proposta foi validada na identificação de arritmias cardíacas em duas bases de dados públicas de ECG (MIT-BIH Arrhythmia e ECG5000). Os resultados revelam competitividade face a técnicas recentes, alcançando métricas AUC de deteção de 93.32% (MIT-BIH Arrhythmia) e 98.84% (ECG5000)

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table