Miracle-dz/UniFAR-FTUs huggingface dataset:
Transformer-Based T2-weighted MRI Synthesis from T1-weighted Images

Multi-modality magnetic resonance (MR) images provide complementary information for disease diagnoses. However, modality missing is quite usual in real-life clinical practice. Current methods usually employ convolution-based generative adversarial network (GAN) or its variants to synthesize the missing modality. With the development of vision transformer, we explore its application in the MRI modality synthesis task in this work. We propose a novel supervised deep learning method for synthesizing a missing modality, making use of a transformer-based encoder. Specifically, a model is trained for translating 2D MR images from T1-weighted to T2-weighted based on conditional GAN (cGAN). We replace the encoder with transformer and input adjacent slices to enrich spatial prior knowledge. Experimental results on a private dataset and a public dataset demonstrate that our proposed model outperforms state-of-the-art supervised methods for MR image synthesis, both quantitatively and qualitatively. Clinical relevance— This work proposes a method to synthesize T2-weighted images from T1-weighted ones to address the modality missing issue in MRI.
Improving the Accuracy of R-Peak Detection in a Wearable Armband Device for Daily Life Electrocardiogram Monitoring Using a Deep Convolutional Denoising Encoder-Decoder Network

Continuous long-term heart rate (HR) monitoring using wearable devices is desirable to aid in the diagnosis of many health-related conditions. Recently, we have developed an armband device that does not use obstructive leads, has dry electrodes which are convenient for long-term electrocardiogram (ECG) recording, and has been shown to be an effective alternate approach for continuous ECG monitoring. However, motion artifacts (MA) due to electromyogram (EMG) contractions are acknowledged as the major challenge of an armband. In this study, we used a deep convolutional neural network denoising encoder-decoder (CNNDED) to enhance the accuracy of R-peak detection in MA-corrupted ECG recordings obtained by an armband device. We collected simultaneous 24-hour ECG recordings using both the armband device and a Holter monitor on 10 subjects. Each 10-sec ECG segment was converted to a time-frequency representation and subsequently used as the input to CNNDED. During the training process, the model learned to accentuate the location of R peaks by amplifying their values in each ECG beat and suppressing the remaining waveforms. For the training output, the model used the R-peak location information from the simultaneously collected Holter ECG data, which were considered as the reference. The performance of CNNDED was evaluated on an independent test data set using the standard performance metrics. The mean relative error of the estimated HR with respect to the Holter data was 17.5 and 7.3 beats/min, pre- and post-CNNDED, respectively. The mean relative difference of the root mean square of successive difference values were 0.23 and 0.06 before and after applying CNNDED, respectively. Although further study is needed, the current preliminary results suggest that CNNDED can improve detection of R peaks even when they are completely buried in the presence of EMG artifacts.
Non-contact Blood Pressure Estimation Method Based on Blood Pressure Category Classification

In recent years, non-contact Blood Pressure (BP) measurement has been attracting attention for measuring our health status in daily life. A Doppler radar can observe pulse waves caused by chest wall displacement due to heartbeat. BP can be estimated by constructing a BP estimation model using BP-related features obtained from the pulse wave. However, compared to when modeling for each subject, the BP esti-mation accuracy deteriorates significantly when modeling with multiple subjects including the testing subject. To deal with this limitation, BP category classification has been introduced into PhotoPlethysmoGraphy (PPG)-based BP estimation. In this paper, we develop a Doppler radar-based BP estimation method based on BP category classification. In the proposed method, the pulse waves extracted from a Doppler radar are classified into three categories, “Low BP”, “Normal BP”, and “High BP” by k-Nearest Neighbor (kNN) based on the features that correlate with BP. The SBP estimation model is trained for each BP category. After the BP category classification, SBP is then estimated by using the model corresponding to the classified BP category. The experimental results showed that the proposed method with BP category classification estimated SBP accurately, compared to without BP category classification.
Design of Optimal Coils for Deep Transcranial Magnetic Stimulation

In this work, a stream function inverse boundary element method (IBEM) has been used for designing different deep transcranial magnetic stimulation (dTMS)coils to activate the prefrontal cortex and the temporal lobe have been set as the target regions. In addition, the performances of these coils have been described and the electric field induced by them has been obtained by using a computational forward technique. These results show that the stream function IBEM is an ideal approach to design optimal dTMS coils capable of producing deep stimulation in the target brain regions. Clinical relevance - The design problem proposed here can be used to produce efficient dTMS stimulators for neurological disorders, which can overcome some of the currently existing limitations of the most common devices employed in TMS.
Non-Contact REM Sleep Estimation Correction by Time-Series Confidence of Predictions: From Binary to Continuous Prediction in Machine Learning for Biological Data

This paper focuses on the REM sleep estimation with bio-vibration data acquired from mattress sensor, and proposes its “correction” method based on Time-Series Confidence (TSC) of the REM sleep prediction calculated by Random Forest (RF) as one of the Machine Learnings (MLs). Unlike the conventional MLs that classify whether the REM sleep or not as its binary prediction, the proposed method determines whether the estimated REM sleep should be corrected or not from its continuous prediction. Concretely, the proposed method computes the REM sleep prediction as the percentage of trees that classify the REM sleep for each epoch (30 seconds), calculates TSC of the REM sleep prediction by windowing the REM sleep prediction of a certain number of epochs to smooth them, and the REM sleep estimated by other MLs is corrected when TSC is lower than a certain threshold. Through the human subject experiments, the following implications have been revealed: (1) the proposed method shows a small TSC in the sudden wrong REM sleep estimation, which contributes to correct it; and (2) because of this feature of the proposed method, the number of False-Positive of the REM sleep estimation is successfully reduced, which improves Precision from 51.4% (w/o TSC) to 59.4% (w/ TSC).
Manually wound coil fabrication process based on cyclic olefin copolymer substrate

Neural prostheses are systems that interact with the human nervous system to recover function lost as a result of disease by recording or stimulating neural signals. Neural implants have a variety of components, but among them, designing highly efficient wireless power transmission systems is particularly important. However, there is a problem that it is difficult to design a wireless power transmission system with high transmission efficiency because power transmission efficiency is affected by various coil parameters. In addition, nerve implants are mainly designed based on polymer substrates, but studies have not been conducted on the process of manufacturing coils based on polymer substrates. In this study, to overcome the mentioned problems, an easy and fast manufacturing process was proposed before designing a precise coil on a polymer substrate. Coil was made by manually winding the 200um copper wire, and the coil was packaged using COC. The coil feasibility test was conducted after fabrication to evaluate the performance of fabrication process. It did not fit the expected frequency because the wire was manually wound to make a small size coil. However, the wire wound coil is expected to be more efficient than the coil made through the PCB process, and if the sophistication is supplemented, it can be used as a highly efficient coil for wireless power transmission.
LAPPD Waveform Response to Annihilation Gammas Incident on BGO Crystals

We have measured the waveform digitized response of a Large Area Picosecond Photon Detector (LAPPD) optically coupled to a 3.0 x 5.4 x 30.0 mm3 BGO scintillator crystal (6-side polished, 5-side wrapped) placed between two LAPPD readout strip regions. We connected four LAPPD signals (two ends each for two anode strips) to a 4GHz Tektronix MS064 Real Time Oscilloscope running at 6.25GS/s and self-triggering. We captured 100000 triggers each with 6250 samples (1 microsecond interval) of waveform per dataset; datasets were obtained with optical grease coupling between the BGO crystal and the LAPPD. We processed these waveforms to produce single-photoelectron pulse lists, with each pulse having a 2D measured position of photoconversion in the LAPPD photocathode. While the LAPPD has sub-millimeter spatial resolution (especially between strips) for normally incident light, the optics of scintillation photon collection with its range of incident angles results in considerable spatial spreading of photons when being transmitted through a 5mm-thick LAPPD fused silica entrance window. Because many independent photon positions are detected, however, the event spatial resolution reduces in quadrature with collected pulses/event to provide a photopeak spatial resolution of <15mm) FWHM each photoelectron.
A Framework for Model Sailing Simulation in Gazebo

Recently there has been increased demand in wind power usage in maritime transportation. At the same time, due to numerous advances, interest in autonomous mobile platforms is at its all-time peak. The utilization of green Unmanned Surface Vehicle fleets could provide enormous benefits in research, security and transportation. The development of autonomous sailing vehicles can be greatly assisted with the usage of simulation where the control, planning and navigation systems can be tested and implemented. Furthermore, great synergy can be achieved through integration of software systems of such platforms with existing popular robotic frameworks, such as ROS. In this paper we propose a flexible ROS-based framework for simulation of customizable sailing vessels, focusing on small scale models.
Unsupervised Domain Adaptation Using Adversarial Learning and Maximum Square Loss for Liver Tumors Detection in Multi-phase CT Images

Automatic and efficient liver tumor detection in multi-phase CT images is essential in computer-aided diagnosis of liver tumors. Nowadays, deep learning has been widely used in medical applications. Normally, deep learning-based AI systems need a large quantity of training data, but in the medical field, acquiring sufficient training data with high-quality annotations is a significant challenge. To solve the lack of training data issue, domain adaptation-based methods have recently been developed as a technique to bridge the domain gap across datasets with different feature characteristics and data distributions. This paper presents a domain adaptation-based method for detecting liver tumors in multi-phase CT images. We adopt knowledge for model learning from PV phase images to ART and NC phase images. Clinical Relevance— To minimize the domain gap we employ an adversarial learning scheme with the maximum square loss for mid-level output feature maps using an anchorless detector. Experiments show that our proposed method performs much better for various CT-phase images than normal training
The Reconstruction Method Using Compressed Sensing and Convolutional Neural Network for PROPELLER MRI in Head

PROPELLER MRI is a method of reconstruction from the collected data of rectangular regions (blades) rotating around the origin of the k-space. This method can compensate for the motion of the subject by using the phase and rotation between the blades. Collecting more blades will improve the accuracy of the correction but will increase the imaging time. On the other hand, reducing the number of phase encodings and blades for shortening the imaging time results in streak artifacts. For this study, we attempted to improve the image quality of the PROPELLER MRI in head by reconstructing using less data with compressed sensing (CS) and a convolutional neural network (CNN), which is a deep-learning method. For k-space data with sampling rates of 11% to 54% by varying the width and number of blades, we compared three patterns of the reconstruction method: A) with only CS, B) with only CNN, and C) with both CS and CNN. For all sampling rates, the method with CS and CNN yielded the best evaluation value; therefore, it is suggested that the image quality could be improved by reconstructing using CS and CNN when the sampling rate is low.
DOI- and TOF-Capable PET Array Detectors Using Double-Ended Light Readout and Stripline-Based Row and Column Electronics Readout

We investigated a highly multiplexing PET detector design capable of depth-of-interaction (DOI) and time-of-flight (TOF) detection by use of double-ended light readout of the scintillator and stripline (SL) based row- and column-wise electronic readout of the SiPMs [1]. We developed 4x4 and 8x8 evaluation modules consisting of 3.0x3. 0x20 mm3 LYSO crystals that were coupled to SiPM pixels at both the front and back ends. For each LYSO crystal, its 4 long surfaces were grounded (9 μm) and covered with BaSO4 for light reflection. The SiPM outputs at the front-end of the crystal array were read by a single SL that was designed to resolve the row position of the detected signal. Likewise, the SiPMs at the back-end were read by a column-resolving SL. As a result, a module had a total of 4 output signals and they were sampled at 2 GHz by use of the DRS4 evaluation board. Using these modules, we reported successful discrimination of individual detector pixels. For the 4x4 module, we obtained an average pixel-level energy resolution (ER) of 14.1% (ranging from 11.3% to 19.3%), a DOI resolution of about 2.7 mm, and a best coincidence time resolution (CTR) of about 391 ps with a reference detector. For the 8x8 module, the average pixel-level ER and DOI resolutions were slightly inferior to those measured for 4x4 module, yielding 13.9% (ranging from 11.7%-17.6%) and about 3.5 mm respectively. However, the best CTR deteriorates considerably to about 584 ps. Our preliminary data suggests that the CTR degradation may be due to intercrystal scattering of the gamma-ray photon.
Dual Discriminator-Based Unsupervised Domain Adaptation Using Adversarial Learning for Liver Segmentation on Multiphase CT Images

Multiphase computed tomography (CT) images are widely used for the diagnosis of liver disease. Since each phase has different contrast enhancement (i.e., different domain), the multiphase CT images should be annotated for all phases to perform liver or tumor segmentation, which is a time-consuming and labor-expensive task. In this paper, we propose a dual discriminator-based unsupervised domain adaptation (DD-UDA) for liver segmentation on multiphase CT images without annotations. Our framework consists of three modules: a task-specific generator and two discriminators. We have performed domain adaptation at two levels: one is at the feature level, and the other is at the output level, to improve accuracy by reducing the difference in distributions between the source and target domains. Experimental results using public data (PV phase only) as the source domain and private multiphase CT data as the target domain show the effectiveness of our proposed DD-UDA method. Clinical relevance— This study helps to efficiently and accurately segment the liver on multiphase CT images, which is an important preprocessing step for diagnosis and surgical support. By using the proposed DD-UDA method, the segmentation accuracy has improved from 5%, 8%, and 6% respectively, for all phases of CT images with comparison to those without UDA.
Computational Drug Target Prediction: Benchmark and Experiments

Despite the development of a range of biological tests that greatly improved the efficiency of drug screening, it is still considered laborious and expensive to screen a potentially vast number of drug-target protein combinations with biological tests. As such, computational (in silico) methods have become popular and are commonly applied for pre-screening as they are capable to conduct efficient screening with fewer resources. This tutorial will provide participants with experience in conducting computational experiments for drug target predictions. In the tutorial, the participants will firstly theoretically review the history of computational drug target prediction. The methods, datasets, and how the experiments are designed will be introduced. Later, the participants will be introduced to a data set, Linked Multipartite Network (LMN), a heterogeneous network that incorporates 12 repositories and includes 7 types of biomedical entities (#20,119 entities, # 194,296 associations). The participants will learn how to use LMN to facilitate drug target prediction, including computational validation, and facilitate scientific discovery. Finally, participants will be introduced to some state-of-the-art computational methods, and practice the adoption of these methods to conduct the experiments by running the tasks with the given training and testing files generated in the LMN. Through the proposed tutorial, participants, such as researchers and trainees, will understand the process of computational drug-target prediction and further learn how to adopt LMN as the dataset to facilitate the drug target prediction in practice as well as apply those skills in future studies.
ExpressionHop: A Lightweight Human Facial Expression Classifier

A lightweight human facial expression recognition (FER) solution aiming at mobile/edge applications is proposed in this work. The solution, called ExpressionHop, consists offour modules: 1) cropping out patches based on facial landmarks, 2) applying filter banks to each patch to generate a rich set of joint spatial-spectral features, 3) conducting the discriminant feature test (DFT) to select features with higher discriminant power, and 4) performing the final classification task with a classifier. We conduct performance benchmarking on ExpressionHop, traditional and deep learning methods on several commonly used FER datasets such as JAFFE, CK + and KDEF. Experimental results show that ExpressionHop achieves comparable or better classification accuracy. Yet, its model size only has 30K parameters, which is significantly lower than those of deep learning methods.
Analysis of Advanced Siamese Neural Networks for Motion Tracking of Sonography of Carotid Arteries

The Siamese Tracker (ST) for tracking objects of interest in Ultrasound (US) images does not incorporate video specific cues and assumes a fixed template of the reference block. Recently, a more advanced version of ST, Correlation Filter Network (CFNet), which overcomes the problems of ST, has been used for tracking in US images. In this study, we demonstrate how the basic CFNet can be made computationally more efficient by reducing the number of layers in its feature extraction network. We further show that due to the unique architecture of the CFNet, this strategy does not affect the performance of the baseline CFNet considerably. Our methodology was evaluated on 10 random sequences from the publicly available carotid artery dataset. CFNet obtained a 35.7% improvement in the average localization error over the basic ST, thus demonstrating that it is a practical and robust tracking algorithm for tracking objects in US images.
Impact of Machine Learning Over Light Weight Conversation Mechanism for Social Chat Group Using IOT Hardware

Considering growing need of social chat group, it has been observed that such mechanism should be more secure and light weighted using IOT Hardware. The focus of proposed work is to increase the performance of light weight conversation mechanism for social chat group by integrating machine learning along with compression and encryption. Conventional cryptography suffers from a number of issues that can be alleviated by lightweight methods. Size of data, processing requirements, memory limitation and energy consumption all are falling under this category. As a replacement for conventional cryptography, this paper outlines many of the techniques that was defined as lightweight algorithms and discusses some trends in the design of these algorithms. A major goal of the proposed research was to verify the accuracy of light weight conversation mechanism for social chat group. In this study, the precision of the proposed work was compared to that of previous work.
A sEMG Proportional Control for the Gripper of Patient Side Manipulator in da Vinci Surgical System

There is a large community of people with hand disabilities, and these disabilities can be a barrier to those looking to retain or pursue surgical careers. With the development of surgical robotics technologies, it may be possible to develop user interfaces to accommodate these individuals. This paper proposes a hand-free control method for the gripper of a patient side manipulator (PSM) in the da Vinci surgical system. Using electromyography (EMG) signals, a proportional control method was tested on its ability to grasp a pressure sensor. These preliminary results demonstrate that the user can reliably control the grasping motion of the da Vinci PSM using this system. There is a strong correlation between grasping force and normalized EMG signal (r= 0.874). Moreover, the gripper can generate a step grasping force output when feeding in a generated step signal. The results in this paper demonstrate the system integration of a research EMG system with the da Vinci surgical system and are a step towards developing accessible teleoperation systems for surgeons with disabilities. Hand-free control for remaining degrees of freedom in the PSM is under development using additional input from the motion capture system.
Real-Time Generation of Hyperbolic Neuronal Spiking Patterns

Neuronal spikes are referred to as the electric activity of neurons (in terms of voltage) in response to various biological events such as the sodium and calcium ionic current channels in the brain. Currently, both biological models as well as mathematical models of neuronal spiking patterns have been introduced in the literature. However, very little attempt has been made to run these models in real-time. With applications ranging from hardware neuromorphic circuit designs, artificial intelligence (AI) architectures, to deep brain stimulation, real-time generation of these models is of particular interest in the brain-inspired computing/architecture and neuro-modulation/stimulation research communities. This paper proposes the development of a framework for generating the hyperbolic based single neuronal spiking patterns in real-time. Simulation results confirm that the generated spikes resemble the existing models of neuronal spiking patterns, with additional real-time run capability as well as the ability to change the parameters on the fly. Clinical relevance—Real-time models of neuronal spiking patterns have significant clinical relevance with respect to applications of neuromorphic/AI chips for medical image processing/computer vision, as well as clinical neuroscience, neuromodulation and neurostimulation such as deep brain stimulation for modulating the abnormal effects of neurological diseases.