Marios S Pattichis

University of New Mexico | UNM · Department of Electrical and Computer Engineering

Machine learning promises to assist physicians with predictions of mortality and of other future clinical events by learning complex patterns from historical data, such as longitudinal electronic health records. Here we show that a convolutional neural network trained on raw pixel data in 812,278 echocardiographic videos from 34,362 individuals pro...

The manuscript describes fast and scalable architecturesand associated algorithms for computing convolutions and cross-correlations. The basic idea is to map 2D convolutions and cross-correlations to a collection of 1D convolutions and cross-correlations in the transform domain. This is accomplished through the use of the Discrete Periodic Radon Tr...

By 2022, we expect video traffic to reach 82% of the total internet traffic. Undoubtedly, the abundance of video-driven applications will likely lead internet video traffic percentage to a further increase in the near future, enabled by associate advances in video devices' capabilities. In response to this ever-growing demand, the Alliance for Open...

Medical image analysis methods require the use of effective representations for differentiating between lesions, diseased regions, and normal structure. Amplitude Modulation - Frequency Modulation (AM-FM) models provide effective representations through physically meaningful descriptors of complex non-stationary structures that can differentiate be...

In this paper, we propose the use of multiscale amplitude-modulation-frequency-modulation (AM-FM) methods for discriminating between normal and pathological retinal images. The method presented in this paper is tested using standard images from the early treatment diabetic retinopathy study. We use 120 regions of 40 x 40 pixels containing four type...

The objective of this work was the investigation of multiscale Amplitude Modulation-Frequency Modulation (AM-FM) analysis based on Difference of Gaussians (DoG) filterbanks representations in order to predict the risk of stroke by analysing carotid plaques ultrasound images of individuals with asymp-tomatic carotid stenosis. We computed the instant...

The papers in this special section focus on large-scale medical imaging and video analytics for clinical decision support systems. Biomedical images and videos are ubiquitous and overwhelming in volume, amounting to a database that can be measured in zettabytes.With increased access to open image and video datasets and the recent development of eff...

italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Background : Monitoring disease evolution in Multiple sclerosis (MS) subjects may aid in decision making for personalizing treatment and disease evolution prediction. We investigate the use of disability progression, using clinical features, the expand...

This review of our experience in computer-assisted tissue image analysis (CATIA) research shows that significant information can be extracted and used to diagnose and distinguish normal from abnormal endometrium. CATIA enabled the evaluation and differentiation between the benign and malignant endometrium during diagnostic hysteroscopy. The efficac...

The present study proposes an adaptive video delivery methodology that enhances quality of experience (QoE) and aids towards the wider adoption of wireless medical video communication systems in standard clinical practice.

At present, there is no established set of ultrasonic features that can identify all the potentially unstable and high risk atherosclerotic carotid plaques in asymptomatic patients. The degree of stenosis is still the main criterion used to decide whether carotid endarterectomy is needed, but it has now been shown to be inaccurate. The overall obje...

SINGLE -STAGE HARDWARE SORTING BLOCKS AND ASSOCIATED MULTIWAY MERGE SORTING NETWORK

Large-scale training of Convolutional Neural Networks (CNN) is extremely demanding in terms of computational resources. Also, for specific applications, the standard use of transfer learning also tends to require far more resources than what may be needed. This work examines the impact of using AM-FM representations as input images for CNN classifi...

Background / Context: After-school programs that focus on integrating computer programming and mathematics in authentic environments are seldomly accessible to students from culturally and linguistically diverse students, in particular bilingual Latina students in rural contexts. Providing a context that broadens Latina students’ participation in m...

Background / Context: Computer Programming is rarely accessible to K-12 students, especially for those from culturally and linguistically diverse backgrounds. Middle school age is a transitioning time when adolescents are more likely to make long-term decisions on their academic choices and interests. Having access to productive and positive knowle...

p>In hardware such as FPGAs, Kenneth Batcher’s Odd-Even Merge Sort and Bitonic Merge Sort are the state-of-the-art methodologies used to quickly sort a list of more than 16 input values. Both sorting networks feature merges of 2 sorted input lists into a single sorted output list. For both, a full sort of 64 and 512 input values requires 21 and...

p>In hardware such as FPGAs, Kenneth Batcher’s Odd-Even Merge Sort and Bitonic Merge Sort are the state-of-the-art methodologies used to quickly sort a list of more than 16 input values. Both sorting networks feature merges of 2 sorted input lists into a single sorted output list. For both, a full sort of 64 and 512 input values requires 21 and...

We have recently introduced a general system for building fast single-stage hardware N-sorters and N-filters, with N>=3. When N-sorter/N-filters designed with this system are constructed using a design logic block in the FPGA central to the Amazon AWS EC2 F1 instance, they were shown to be significantly faster than the prior state-of-the-art sortin...

The paper presents the results from a multi-year effort to develop and validate image processing methods for selecting the best physical models based on solar image observations. The approach consists of selecting the physical models based on their agreement with coronal holes extracted from the images. Ultimately, the goal is to use physical model...

Speaker diarization refers to methods for identifying speakers from audio recordings. An important application comes from the need to assess student interactions in collaborative learning environments. Diarization is very difficult in these environments where a single microphone is used to record multiple voices. Although there have been advancemen...

In hardware such as FPGAs, Kenneth Batcher’s Odd-Even Merge Sort and Bitonic Merge Sort are the state-of-the-art methodologies used to quickly sort a list of more than 16 input values. Both sorting networks feature merges of 2 sorted input lists into a single sorted output list. For both, a full sort of 64 and 512 input values requires 21 and 45 se...

The authors’ recently published design system for the creation of single-stage N-sorter/N-filter sorting devices, which were implemented using a particular example hardware block, is here expanded and applied to a second hardware type, FPGA carry chain logic. Although several researchers have published applications which use FPGA carry chain logic,...

Speech recognition is very challenging in student learning environments that are characterized by significant cross-talk and background noise. To address this problem, we present a bilingual speech recognition system that uses an interactive video analysis system to estimate the 3D speaker geometry for realistic audio simulations. We demonstrate th...

The Discrete Periodic Radon Transform (DPRT) has been extensively used in applications that involve image reconstructions from projections. This manuscript introduces a fast and scalable approach for computing the forward and inverse DPRT that is based on the use of: (i) a parallel array of fixed-point adder trees, (ii) circular shift registers to...

The manuscript describes fast and scalable architectures and associated algorithms for computing convolutions and cross-correlations. The basic idea is to map 2D convolutions and cross-correlations to a collection of 1D convolutions and cross-correlations in the transform domain. This is accomplished through the use of the Discrete Periodic Radon T...

We introduce the problem of detecting a group of students from classroom videos. The problem requires the detection of students from different angles and the separation of the group from other groups in long videos (one to one and a half hours). We use multiple image representations to solve the problem. We use FM components to separate each group...

Face recognition in collaborative learning videos presents many challenges. In collaborative learning videos, students sit around a typical table at different positions to the recording camera, come and go, move around, get partially or fully occluded. Furthermore, the videos tend to be very long, requiring the development of fast and accurate meth...

We study the problem of detecting talking activities in collaborative learning videos. Our approach uses head detection and projections of the log-magnitude of optical flow vectors to reduce the problem to a simple classification of small projection images without the need for training complex, 3-D activity classification systems. The small project...

Long-term object detection requires the integration of frame-based results over several seconds. For non-deformable objects, long-term detection is often addressed using object detection followed by video tracking. Unfortunately, tracking is inapplicable to objects that undergo dramatic changes in appearance from frame to frame. As a related exampl...

Speech recognition is very challenging in student learning environments that are characterized by significant cross-talk and background noise. To address this problem, we present a bilingual speech recognition system that uses an interactive video analysis system to estimate the 3D speaker geometry for realistic audio simulations. We demonstrate th...

Face recognition in collaborative learning videos presents many challenges. In collaborative learning videos, students sit around a typical table at different positions to the recording camera, come and go, move around, get partially or fully occluded. Furthermore, the videos tend to be very long, requiring the development of fast and accurate meth...

We study the problem of detecting talking activities in collaborative learning videos. Our approach uses head detection and projections of the log-magnitude of optical flow vectors to reduce the problem to a simple classification of small projection images without the need for training complex, 3-D activity classification systems. The small project...

Long-term object detection requires the integration of frame-based results over several seconds. For non-deformable objects, long-term detection is often addressed using object detection followed by video tracking. Unfortunately, tracking is inapplicable to objects that undergo dramatic changes in appearance from frame to frame. As a related exampl...

System and methods for the joint control of reconstructed video quality , computational complexity and compression rate for intra-mode and inter-mode video encoding in HEVC. The invention provides effective methods for (i) generating a Pareto front for intra-coding by varying CTU parameters and the QP , (ii) generating a Pareto front for inter-codi...

By 2022, we expect video traffic to reach 82% of the total internet traffic. Undoubtedly, the abundance of video-driven applications will likely lead internet video traffic percentage to a further increase in the near future, enabled by associate advances in video devices' capabilities. In response to this ever-growing demand, the Alliance for Open...

There is strong interest in developing high-performance hardware sorting systems which can sort a set of elements as quickly as possible. The fastest of the current FPGA systems are sorting networks, in which sets of 2-sorters operate in parallel in each series stage of a multi-stage sorting process. A 2-sorter is a single-stage hardware block whic...

Unsupervised latent variable models—blind source separation (BSS) especially—enjoy a strong reputation for their interpretability. But they seldom combine the rich diversity of information available in multiple datasets, even though multidatasets yield insightful joint solutions otherwise unavailable in isolation. We present a direct, principled ap...

The prevalence of video driven applications, leveraging over the top video on demand services as well as live video streaming applications, dominate network traffic over today’s internet landscape. As such, they necessitate efficient video compression methods to accommodate the desired quality of service and hence user experience. In this study, we...

Video compression is the core technology in mobile (mHealth) and electronic (eHealth) health video streaming applications. With global video traffic projected to reach 82% of all Internet traffic by 2022, there is a strong need to develop efficient compression algorithms to accommodate expected future growth. For the first time in decades, and espe...

In the last two decades, unsupervised latent variable models---blind source separation (BSS) especially---have enjoyed a strong reputation for the interpretable features they produce. Seldom do these models combine the rich diversity of information available in multiple datasets. Multidatasets, on the other hand, yield joint solutions otherwise una...

The paper presents the results from a multi-year effort to develop and validate image processing methods for selecting the best physical models based on solar image observations. The approach consists of selecting the physical models based on their agreement with coronal holes extracted from the images. Ultimately, the goal is to use physical model...

Video compression is the core technology in mobile (mHealth) and electronic (eHealth) health video streaming applications. With global video traffic projected to reach 82 % of all internet traffic by 2022, both industry and academia are struggling to develop efficient compression algorithms to match these unprecedented needs. To the best of our kno...

Precision medicine promises better healthcare delivery by improving clinical practice. Using evidence-based sub-stratification of patients, the objective is to achieve better prognosis, diagnosis, and treatment that will transform existing clinical pathways towards optimizing care for the specific needs of each patient. The wealth of today's health...

This study investigates the compression efficiency of well established (H.265 and VP9), recently standardized (AV1), and emerging (VVC) video codecs and their applicability in the healthcare domain. Preliminary results using an ultrasound video dataset show that VVC achieves the best encoding performance, closely followed by AV1 and HM. To the best...

Whilst Science, Technology, Engineering and Mathematics (STEM) interdisciplinary teaching and learning in the USA K-12 education still needs greater promotion, middle school students demonstrated that they can, using low-cost, single board computers that promote the teaching of computer science (in this case Raspberry Pis), successfully engage with...

Over the solar-activity cycle, there are extended periods where significant discrepancies occur between the spacecraft-observed total (unsigned) open magnetic flux and that determined from coronal models. In this article, the total open heliospheric magnetic flux is computed using two different methods and then compared with results obtained from i...

We present an interpretable neural network for predicting an important clinical outcome (1-year mortality) from multi-modal Electronic Health Record (EHR) data. Our approach builds on prior multi-modal machine learning models by now enabling visualization of how individual factors contribute to the overall outcome risk, assuming other factors remai...

Cyprus’s goal is to deliver high quality cross border healthcare for both its citizens living abroad as well as visiting citizens from other EU Member States.

This study proposes an adaptive video encoding framework based on multi-objective optimization that jointly maximizes the encoded video’s (clinical) quality and encoding rate (in frames per second) while minimizing bitrate demands.

The wider adoption of mobile Health (mHealth) video communication systems in standard clinical practice requires real-time control to provide for adequate levels of clinical video quality to support reliable diagnosis. The latter can only be achieved with real-time adaptation to time-varying wireless networks’ state to guarantee clinically acceptab...

We present a framework for adaptive video encoding based on video content. The basic idea is to analyze the video to determine camera activity (tracking, stationary, or zooming) and then associate each activity with adaptive video quality constraints. We demonstrate our approach on the UT LIVE video quality assessment database. We show that effecti...

We present a framework for adaptive video encoding based on video content. The basic idea is to analyze the video to determine camera activity (tracking, stationary, or zooming) and then associate each activity with adaptive video quality constraints. We demonstrate our approach on the UT LIVE video quality assessment database. We show that effecti...

We present a framework for adaptive video encoding based on video content. The basic idea is to analyze the video to determine camera activity (tracking, stationary, or zooming) and then associate each activity with adaptive video quality constraints. We demonstrate our approach on the UT LIVE video quality assessment database. We show that effecti...

This paper presents an electronic registry system for the purposes of the rare congenital conditions that require lifelong follow up and treatment. The main objective of the eRegistry focuses on the prevention of major rare anaemias (RAs) by facilitating the access, at a European level, to the best genetic counselling, diagnosis and clinical manage...

There is a growing need for developing fast and scalable methods for storing and processing large databases of healthcare images and videos. The paper reviews current medical image analysis techniques and the recent emergence, promise, and challenges associated with large scale video analysis methods. Furthermore, the paper describes large-scale vi...

The paper proposes an open-source, maintainable system for detecting human activity in video datasets using scalable hardware architectures. The system is validated by detecting writing and typing activities that were collected as part of the Advancing Out of School Learning in Mathematics and Engineering (AOLME) project. The implementation of the...

We present a new approach for motion estimation from digital videos based on the use of 2D amplitude-modulation frequency-modulation (AM-FM) models. The proposed approach uses an AM-FM representation to derive AM and FM based equations that can be applied to two consecutive frames to derive motion estimates. We test the proposed method using comple...

This study proposes an end-to-end mobile tele-echography platform using a portable robot for remote cardiac ultrasonography. Performance evaluation investigates the capacity of LTE wireless networks to facilitate responsive robot tele-manipulation and real-time ultrasound video streaming that qualifies for clinical practice. Within this context, a...

In the past decade, numerous advances in the study of the human brain were fostered by successful applications of blind source separation (BSS) methods to a wide range of imaging modalities. The main focus has been on extracting “networks” represented as the underlying latent sources. While the broad success in learning latent representations from...

The wider adoption of mHealth video communication systems in standard clinical practice requires adequate levels of clinical video quality to support reliable diagnosis. The latter dictates that real-time adaptation to time-varying wireless networks’ state to guarantee clinically acceptable performance throughout the streaming session, while confor...