Roadmap on signal processing for next generation measurement systems

Signal processing is a fundamental component of almost any sensor-enabled system, with a wide range of applications across different scientific disciplines. Time series data, images, and video sequences comprise representative forms of signals that can be enhanced and analysed for information extraction and quantification. The recent advances in artificial intelligence and machine learning are shifting the research attention towards intelligent, data-driven, signal processing. This roadmap presents a critical overview of the state-of-the-art methods and applications aiming to highlight future challenges and research opportunities towards next generation measurement systems. It covers a broad spectrum of topics ranging from basic to industrial research, organized in concise thematic sections that reflect the trends and the impacts of current and future developments per research field. Furthermore, it offers guidance to researchers and funding agencies in identifying new prospects.AerodynamicsMicrowave Sensing, Signals & System

Deep Active Learning for Automatic Mitotic Cell Detection on HEp-2 Specimen Medical Images

Identifying Human Epithelial Type 2 (HEp-2) mitotic cells is a crucial procedure in anti-nuclear antibodies (ANAs) testing, which is the standard protocol for detecting connective tissue diseases (CTD). Due to the low throughput and labor-subjectivity of the ANAs' manual screening test, there is a need to develop a reliable HEp-2 computer-aided diagnosis (CAD) system. The automatic detection of mitotic cells from the microscopic HEp-2 specimen images is an essential step to support the diagnosis process and enhance the throughput of this test. This work proposes a deep active learning (DAL) approach to overcoming the cell labeling challenge. Moreover, deep learning detectors are tailored to automatically identify the mitotic cells directly in the entire microscopic HEp-2 specimen images, avoiding the segmentation step. The proposed framework is validated using the I3A Task-2 dataset over 5-fold cross-validation trials. Using the YOLO predictor, promising mitotic cell prediction results are achieved with an average of 90.011% recall, 88.307% precision, and 81.531% mAP. Whereas, average scores of 86.986% recall, 85.282% precision, and 78.506% mAP are obtained using the Faster R-CNN predictor. Employing the DAL method over four labeling rounds effectively enhances the accuracy of the data annotation, and hence, improves the prediction performance. The proposed framework could be practically applicable to support medical personnel in making rapid and accurate decisions about the mitotic cells' existence

Microscopy and Analysis

Microscopes represent tools of the utmost importance for a wide range of disciplines. Without them, it would have been impossible to stand where we stand today in terms of understanding the structure and functions of organelles and cells, tissue composition and metabolism, or the causes behind various pathologies and their progression. Our knowledge on basic and advanced materials is also intimately intertwined to the realm of microscopy, and progress in key fields of micro- and nanotechnologies critically depends on high-resolution imaging systems. This volume includes a series of chapters that address highly significant scientific subjects from diverse areas of microscopy and analysis. Authoritative voices in their fields present in this volume their work or review recent trends, concepts, and applications, in a manner that is accessible to a broad readership audience from both within and outside their specialist area

A CAD system for early diagnosis of autism using different imaging modalities.

The term “autism spectrum disorder” (ASD) refers to a collection of neuro-developmental disorders that affect linguistic, behavioral, and social skills. Autism has many symptoms, most prominently, social impairment and repetitive behaviors. It is crucial to diagnose autism at an early stage for better assessment and investigation of this complex syndrome. There have been a lot of efforts to diagnose ASD using different techniques, such as imaging modalities, genetic techniques, and behavior reports. Imaging modalities have been extensively exploited for ASD diagnosis, and one of the most successful ones is Magnetic resonance imaging(MRI),where it has shown promise for the early diagnosis of the ASD related abnormalities in particular. Magnetic resonance imaging (MRI) modalities have emerged as powerful means that facilitate non-invasive clinical diagnostics of various diseases and abnormalities since their inception in the 1980s. After the advent in the nineteen eighties, MRI soon became one of the most promising non- invasive modalities for visualization and diagnostics of ASD-related abnormalities. Along with its main advantage of no exposure to radiation, high contrast, and spatial resolution, the recent advances to MRI modalities have notably increased diagnostic certainty. Multiple MRI modalities, such as different types of structural MRI (sMRI) that examines anatomical changes, and functional MRI (fMRI) that examines brain activity by monitoring blood flow changes,have been employed to investigate facets of ASD in order to better understand this complex syndrome. This work aims at developing a new computer-aided diagnostic (CAD) system for autism diagnosis using different imaging modalities. It mainly relies on making use of structural magnetic resonance images for extracting notable shape features from parts of the brainthat proved to correlate with ASD from previous neuropathological studies. Shape features from both the cerebral cortex (Cx) and cerebral white matter(CWM)are extracted. Fusion of features from these two structures is conducted based on the recent findings suggesting that Cx changes in autism are related to CWM abnormalities. Also, when fusing features from more than one structure, this would increase the robustness of the CAD system. Moreover, fMRI experiments are done and analyzed to find areas of activation in the brains of autistic and typically developing individuals that are related to a specific task. All sMRI findings are fused with those of fMRI to better understand ASD in terms of both anatomy and functionality,and thus better classify the two groups. This is one aspect of the novelty of this CAD system, where sMRI and fMRI studies are both applied on subjects from different ages to diagnose ASD. In order to build such a CAD system, three main blocks are required. First, 3D brain segmentation is applied using a novel hybrid model that combines shape, intensity, and spatial information. Second, shape features from both Cx and CWM are extracted and anf MRI reward experiment is conducted from which areas of activation that are related to the task of this experiment are identified. Those features were extracted from local areas of the brain to provide an accurate analysis of ASD and correlate it with certain anatomical areas. Third and last, fusion of all the extracted features is done using a deep-fusion classification network to perform classification and obtain the diagnosis report. Fusing features from all modalities achieved a classification accuracy of 94.7%, which emphasizes the significance of combining structures/modalities for ASD diagnosis. To conclude, this work could pave the pathway for better understanding of the autism spectrum by finding local areas that correlate to the disease. The idea of personalized medicine is emphasized in this work, where the proposed CAD system holds the promise to resolve autism endophenotypes and help clinicians deliver personalized treatment to individuals affected with this complex syndrome

Advanced Photothermal Optical Coherence Tomography (PT-OCT) for Quantification of Tissue Composition

Optical coherence tomography (OCT) is an imaging technique that forms 2D or 3D images of tissue structures with micron-level resolution. Today, OCT systems are widely used in medicine, especially in the fields of ophthalmology, interventional cardiology, oncology, and dermatology. Although OCT images provide insightful structural information of tissues, these images are not specific to the chemical composition of the tissue. Yet, chemical tissue composition is frequently relevant to the stage of a disease (e.g., atherosclerosis), leading to poor diagnostic performance of structural OCT images. Photo-thermal optical coherence tomography (PT-OCT) is a functional extension of OCT with the potential to overcome this shortcoming by overlaying the 3D structural images of OCT with depth-resolved light absorption information. Potentially, signal analysis of the light absorption maps can be used to obtain refined insight into the chemical composition of tissue. Such analysis, however, is complex because the underlying physics of PT-OCT is multifactorial. Aside from tissue chemical composition, the optical, thermal, and mechanical properties of tissue affect PT-OCT signals; system/instrumentation parameters also influence PT-OCT signals. As such, obtaining refined insight into tissue chemical composition requires in-depth research aimed at answering several key unknowns and questions about this technique. The goal of this dissertation is to generate in-depth knowledge on sample and system parameters affecting PT-OCT signals, to develop strategies for optimal detection of a molecule of interest (MOI) and potentially for its quantification, and to improve the imaging rate of the system. The following items are major outcomes of this dissertation: 1- Generated comprehensive theory that discovers relations between sample/tissue properties and experimental conditions and their multifactorial effects on PT-OCT signals. 2- Developed system and experimentation strategies for detection of multiple molecules of interest with high specificity. 3- Generated optimized machine learning-powered model, in light of the above two outcomes, for automated depth-resolved interpretation of tissue composition from PT-OCT images. 4- Increased the imaging rate of PT-OCT by orders of magnitude by introducing a new variant of PT-OCT based on pulsed photothermal excitation. 5- Developed algorithms for signal denoising and improving the quality of received signals and the contrast in images which in return enables faster PT-OCT imaging

Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9 International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications

Advanced Photothermal Optical Coherence Tomography (PT-OCT) for Quantification of Tissue Composition

Optical coherence tomography (OCT) is an imaging technique that forms 2D or 3D images of tissue structures with micron-level resolution. Today, OCT systems are widely used in medicine, especially in the fields of ophthalmology, interventional cardiology, oncology, and dermatology. Although OCT images provide insightful structural information of tissues, these images are not specific to the chemical composition of the tissue. Yet, chemical tissue composition is frequently relevant to the stage of a disease (e.g., atherosclerosis), leading to poor diagnostic performance of structural OCT images. Photo-thermal optical coherence tomography (PT-OCT) is a functional extension of OCT with the potential to overcome this shortcoming by overlaying the 3D structural images of OCT with depth-resolved light absorption information. Potentially, signal analysis of the light absorption maps can be used to obtain refined insight into the chemical composition of tissue. Such analysis, however, is complex because the underlying physics of PT-OCT is multifactorial. Aside from tissue chemical composition, the optical, thermal, and mechanical properties of tissue affect PT-OCT signals; system/instrumentation parameters also influence PT-OCT signals. As such, obtaining refined insight into tissue chemical composition requires in-depth research aimed at answering several key unknowns and questions about this technique. The goal of this dissertation is to generate in-depth knowledge on sample and system parameters affecting PT-OCT signals, to develop strategies for optimal detection of a molecule of interest (MOI) and potentially for its quantification, and to improve the imaging rate of the system. The following items are major outcomes of this dissertation: 1- Generated comprehensive theory that discovers relations between sample/tissue properties and experimental conditions and their multifactorial effects on PT-OCT signals. 2- Developed system and experimentation strategies for detection of multiple molecules of interest with high specificity. 3- Generated optimized machine learning-powered model, in light of the above two outcomes, for automated depth-resolved interpretation of tissue composition from PT-OCT images. 4- Increased the imaging rate of PT-OCT by orders of magnitude by introducing a new variant of PT-OCT based on pulsed photothermal excitation. 5- Developed algorithms for signal denoising and improving the quality of received signals and the contrast in images which in return enables faster PT-OCT imaging