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Multi-feature Multi-Scale CNN-Derived COVID-19 Classification from Lung Ultrasound Data

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arXiv 2021年

作者： Che, Hui Radbel, Jared Sunderram, Jag Nosher, John L. Patel, Vishal M. Hacihaliloglu, Ilker The Department of Biomedical Engineering Rutgers University NJ United States The Department of Medicine Rutgers Robert Wood Johnson Medical School NJ United States The Department of Radiology Rutgers Robert Wood Johnson Medical School NJ United States The Department of Electrical and Computer Engineering Johns Hopkins University MD United States The Department of Biomedical Engineering Rutgers University The Department of Radiology Rutgers Robert Wood Johnson Medical School NJ United States

The global pandemic of the novel coronavirus disease 2019 (COVID-19) has put tremendous pressure on the medical system. Imaging plays a complementary role in the management of patients with COVID-19. Computed tomography (CT) and chest X-ray (CXR) are the two dominant screening tools. However, difficulty in eliminating the risk of disease transmission, radiation exposure and not being cost-effective are some of the challenges for CT and CXR imaging. This fact induces the implementation of lung ultrasound (LUS) for evaluating COVID-19 due to its practical advantages of noninvasiveness, repeatability, and sensitive bedside property. In this paper, we utilize a deep learning model to perform the classification of COVID-19 from LUS data, which could produce objective diagnostic information for clinicians. Specifically, all LUS images are processed to obtain their corresponding local phase filtered images and radial symmetry transformed images before fed into the multi-scale residual convolutional neural network (CNN). Secondly, image combination as the input of the network is used to explore rich and reliable features. Feature fusion strategy at different levels is adopted to investigate the relationship between the depth of feature aggregation and the classification accuracy. Our proposed method is evaluated on the point-of-care US (POCUS) dataset together with the Italian COVID-19 Lung US database (ICLUS-DB) and shows promising performance for COVID-19 prediction. Copyright © 2021, The Authors. All rights reserved.

关键词： Coronavirus

Feasibility of Inferring Spatial Transcriptomics from Single-Cell Histological Patterns for Studying Colon Cancer Tumor Heterogeneity

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Feasibility of Inferring Spatial Transcriptomics from Single...

18th International Joint Conference on biomedical engineering Systems and Technologies, BIOSTEC 2025

作者： Fatemi, Michael Y. Lu, Yunrui Azher, Zarif L. Sharma, Cyril Feng, Eric Diallo, Alos B. Srinivasan, Gokul Rosner, Grace M. Pointer, Kelli B. Christensen, Brock C. Salas, Lucas A. Tsongalis, Gregory J. Palisoul, Scott M. Perreard, Laurent Kolling, Fred W. Vaickus, Louis J. Levy, Joshua J. University of Virginia CharlottesvilleVA United States Emerging Diagnostic and Investigative Technologies Department of Pathology and Laboratory Medicine Dartmouth Health LebanonNH United States Department of Computer Science Purdue University West LafayetteIN United States Thomas Jefferson High School for Science and Technology AlexandriaVA United States Department of Epidemiology Dartmouth College Geisel School of Medicine HanoverNH United States Program in Quantitative Biomedical Sciences Dartmouth College Geisel School of Medicine HanoverNH United States Department of Medicine Section of Radiology Oncology Dartmouth Health LebanonNH United States Department of Molecular and Cell Biology Dartmouth College Geisel School of Medicine HanoverNH United States Genomics Shared Resource Dartmouth Cancer Center LebanonNH United States Department of Dermatology Dartmouth Health LebanonNH United States Department of Pathology and Laboratory Medicine Cedars Sinai Medical Center Los AngelesCA United States Department of Computational Biomedicine Cedars Sinai Medical Center Los AngelesCA United States Division of Biology and Biological Engineering California Institute of Technology PasadenaCA United States

Spatial transcriptomics (ST) enables studying spatial organization of gene expression within tissues, offering insights into the molecular diversity of tumors. Recent methods have demonstrated the capability to disaggregate this information at subspot resolution by leveraging both expression and histological patterns. Elucidating such information from histology alone presents a significant challenge, but if solved can enable spatial molecular analysis at cellular resolution even where ST data is not available, reducing study costs. This study explores integrating single-cell histological and transcriptomic data to infer spatial mRNA expression patterns in colorectal cancer whole slide images. A cell-graph neural network algorithm was developed to align histological information extracted from detected cells with single cell RNA, facilitating the analysis of cellular groupings and gene relationships. We demonstrate that single-cell transcriptional heterogeneity within a spot could be predicted from histological markers extracted from cells detected within it. Our model exhibited proficiency in delineating overarching gene expression patterns across whole-slide images. This approach compared favorably to traditional computer vision methods which did not incorporate single cell expression during the model training. This innovative approach augments the resolution of spatial molecular assays utilizing histology as sole input through co-mapping of histological and transcriptomic datasets at the single-cell level. © 2025 by SCITEPRESS– Science and Technology Publications, Lda.

关键词： Tumors

Implementation of MPPT technique of solar module with supervised machine learning

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arXiv 2021年

作者： Sharmin, Ruhi Chowdhury, Sayeed Shafayet Abedin, Farihal Rahman, Kazi Mujibur Department of Biomedical Engineering Purdue University 206 S. Martin Jischke Drive West LafayetteIN47907-2032 United States Department of Electrical & Computer Engineering Purdue University 501 Northwestern Ave. West LafayetteIN47907-2035 United States Dhaka Bangladesh

Automated calibration of a maximum power point tracking (MPPT) algorithm and its efficacious implementation for the photo-voltaic (PV) system is pivotal for harnessing maximum possible energy from solar power. However, most existing calibration methods of such an MPPT system are cumbersome and vary greatly with the environmental condition. Hence, an automated pipeline capable of performing suitable adjustments and accurate analysis for solar PV systems is highly desirable. To counter this issue, numerous algorithms have been proposed so far, such as perturb and observe (P&O) method, incremental conductance (IC) method, fractional open circuit voltage (FOCV) method, short circuit current method, fuzzy logic based algorithm etc. While these approaches perform pretty well and produce overall acceptable results, recent surge of data-driven machine learning (ML) approaches hold great promise in this research domain. Since, supervised ML methods are trained directly based on the data, no human-designed heuristics are involved, which makes these techniques highly accurate and robust. As a result, in this paper, we proposed a method using supervised ML in solar PV system for MPPT analysis. For this purpose, an overall schematic diagram of a PV system is designed and simulated to create a dataset in MATLAB/ Simulink. Thus, by analyzing the output characteristics of a solar cell, an improved MPPT algorithm on the basis of neural network (NN) method is put forward to track the maximum power point (MPP) of solar cell modules. To perform the task, Bayesian Regularization method was chosen as the training algorithm as it works best even for smaller data supporting the wide range of the train data set. The theoretical results show that the improved NN MPPT algorithm has higher efficiency compared with the Perturb and Observe method in the same environment, and the PV system can keep working at MPP without oscillation and probability of any kind of misjudgment. So it can not only r

关键词： Maximum power point trackers

MorphoITH: A Framework for Deconvolving Intra-Tumor Heterogeneity Using Tissue Morphology

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arXiv 2025年

作者： Nielsen, Aleksandra Weronika Manoochehri, Hafez Eslami Zhong, Hua Panwar, Vandana Jarmale, Vipul Jasti, Jay Nourani, Mehrdad Rakheja, Dinesh Brugarolas, James Kapur, Payal Rajaram, Satwik Lyda Hill Department of Bioinformatics University of Texas Southwestern Medical Center DallasTX United States Department of Electrical and Computer Engineering The University of Texas at Dallas RichardsonTX United States Department of Pathology University of Texas Southwestern Medical Center DallasTX United States Kidney Cancer Program Simmons Comprehensive Cancer Center University of Texas Southwestern Medical Center DallasTX United States University of Texas Southwestern Medical Center DallasTX United States Department of Urology University of Texas Southwestern Medical Center at Dallas DallasTX United States

The ability of tumors to evolve and adapt by developing subclones in different genetic and epigenetic states is a major challenge in oncology. Traditional tools like multi-regional sequencing used to study tumor evolution and the resultant intra-tumor heterogeneity (ITH) are often impractical because of their resource-intensiveness and limited scalability. Here, we present MorphoITH, a novel framework that leverages histopathology slides to deconvolve molecular ITH through tissue morphology. MorphoITH integrates a self-supervised deep learning similarity measure to capture phenotypic variation across multiple dimensions (cytology, architecture, and microenvironment) with rigorous methods to eliminate spurious sources of variation. Using a prototype of ITH, clear cell renal cell carcinoma (ccRCC), we show that MorphoITH captures clinically-significant biological features, such as vascular architecture and nuclear grades. Furthermore, we find that MorphoITH recognizes differential biological states corresponding to subclonal changes in key driver genes (BAP1/PBRM1/SETD2). Finally, by applying MorphoITH to a multi-regional sequencing experiment, we postulate evolutionary trajectories that largely recapitulate genetic evolution. In summary, MorphoITH provides a scalable phenotypic lens that bridges the gap between histopathology and genomics, advancing precision oncology. © 2025, CC BY-NC-ND.

关键词： Tumors

Explainable semantic space by grounding language to vision with cross-modal contrastive learning

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arXiv 2021年

作者： Zhang, Yizhen Choi, Minkyu Han, Kuan Liu, Zhongming Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States Department of Neurological Surgery University of California San Francisco San FranciscoCA94143 United States Department of Biomedical Engineering University of Michigan Ann ArborMI48109 United States

In natural language processing, most models try to learn semantic representations merely from texts. The learned representations encode the "distributional semantics" but fail to connect to any knowledge about the physical world. In contrast, humans learn language by grounding concepts in perception and action and the brain encodes "grounded semantics" for cognition. Inspired by this notion and recent work in vision-language learning, we design a two-stream model for grounding language learning in vision. The model includes a VGG-based visual stream and a Bert-based language stream. The two streams merge into a joint representational space. Through cross-modal contrastive learning, the model first learns to align visual and language representations with the MS COCO dataset. The model further learns to retrieve visual objects with language queries through a cross-modal attention module and to infer the visual relations between the retrieved objects through a bilinear operator with the Visual Genome dataset. After training, the model's language stream is a stand-alone language model capable of embedding concepts in a visually grounded semantic space. This semantic space manifests principal dimensions explainable with human intuition and neurobiological knowledge. Word embeddings in this semantic space are predictive of human-defined norms of semantic features and are segregated into perceptually distinctive clusters. Furthermore, the visually grounded language model also enables compositional language understanding based on visual knowledge and multimodal image search with queries based on images, texts, or their combinations. © 2021, CC BY.

关键词： Embeddings

Decentralized dynamic state estimation with bimodal Gaussian mixture measurement noise

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arXiv 2020年

作者： Sarfi, Vahid Ghasemkhani, Amir Niazazari, Iman Livani, Hanif Yang, Lei Department of Electrical and Biomedical Engineering University of Nevada Reno United States Department of Computer Science and Engineering University of Nevada Reno United States

This paper proposes a decentralized dynamic state estimation (DSE) algorithm with bimodal Gaussian mixture measurement noise. The decentralized DSE is formulated using the Ensemble Kalman Filter (EnKF) and then compared with the unscented Kalman filter (UKF). The performance of the proposed framework is verified using the WSCC 9-bus system simulated in the Real Time Digital Simulator (RTDS). The phasor measurement unit (PMU) measurements are streamed in real-time from the RTDS runtime environment to MATLAB for real-time visualization and estimation. To consider the data corruption scenario in the streaming process, a bi-modal distribution containing two normal distributions with different weights and variances are added to the measurements as the noise component. The performances of both UKF and EnKF are then compared for by calculating the mean-squared-errors (MSEs) between the actual and estimated states. Copyright © 2020, The Authors. All rights reserved.

关键词： Phasor measurement units

Layer-dependent optically induced spin polarization in InSe

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Physical Review B 2023年第11期107卷 115304-115304页

作者： Jovan Nelson Teodor K. Stanev Dmitry Lebedev Trevor LaMountain J. Tyler Gish Hongfei Zeng Hyeondeok Shin Olle Heinonen Kenji Watanabe Takashi Taniguchi Mark C. Hersam Nathaniel P. Stern Applied Physics Program Northwestern University Evanston Illinois 60208 USA Department of Physics and Astronomy Northwestern University Evanston Illinois 60208 USA Department of Material Science and Engineering Northwestern University Evanston Illinois 60208 USA Computational Science Division Argonne National Laboratory Lemont Illinois 60439 USA Material Science Division Argonne National Laboratory Lemont Illinois 60439 USA Research Center for Functional Materials National Institute for Materials Science 1-1 Namiki Tsukuba 305-0044 Japan International Center for Materials Nanoarchitectonics National Institute for Materials Science 1-1 Namiki Tsukuba 305-0044 Japan Department of Chemistry Northwestern University Evanston Illinois 60208 USA Department of Electrical Engineering and Computer Science Northwestern University Evanston Illinois 60208 USA

Optical control of spin in semiconductors has been pioneered using nanostructures of III-V and II-VI semiconductors, but the emergence of two-dimensional van der Waals materials offers an alternative low-dimensional platform for spintronic phenomena. Indium selenide (InSe), a group-III monochalcogenide van der Waals material, has shown promise for optoelectronics due to its high electron mobility, tunable direct band gap, and quantum transport. In addition to these confirmed properties, there are predictions of spin-dependent optical selection rules, suggesting the potential for all-optical excitation and control of spin in a two-dimensional layered material. Despite these predictions, layer-dependent optical spin phenomena in InSe have yet to be explored. Here, we present measurements of layer-dependent optical spin dynamics in few-layer and bulk InSe. Polarized photoluminescence reveals layer-dependent optical orientation of spin, thereby demonstrating the optical selection rules in few-layer InSe. Spin dynamics are also studied in many-layer InSe using time-resolved Kerr rotation spectroscopy. By applying out-of-plane and in-plane static magnetic fields for polarized emission measurements and Kerr measurements, respectively, the g factor for InSe was extracted. Further investigations are done by calculating precession values using a k·p model, which is supported by ab initio density functional theory. Comparison of predicted precession rates with experimental measurements highlights the importance of excitonic effects in InSe for understanding spin dynamics. Optical orientation of spin is an important prerequisite for optospintronic phenomena and devices, and these first demonstrations of layer-dependent optical excitation of spins in InSe lay the foundation for combining layer-dependent spin properties with advantageous electronic properties found in this material.

关键词： Magneto-optics Spin dynamics Chalcogenides Layered semiconductors Van der Waals systems Density functional theory Kerr effect Photoluminescence k dot p method

Miniaturized Microarray-Format Digital ELISA Enabled by Lithographic Protein Patterning

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SSRN

SSRN 2023年

作者： Stephens, Andrew D. Song, Yujing McClellan, Brandon L. Su, Shiuan-Haur Xu, Sonnet Chen, Kevin Castro, Maria G. Singer, Benjamin H. Kurabayashi, Katsuo Department of Mechanical Engineering University of Michigan Ann ArborMI48109 United States Department of Neurosurgery University of Michigan Medical School Ann ArborMI48109 United States Department of Cell and Developmental Biology University of Michigan Medical School Ann ArborMI48109 United States Graduate Program in Immunology University of Michigan Medical School Ann ArborMI48109 United States Department of Biomedical Engineering University of Michigan Ann ArborMI48109 United States Rogel Cancer Center University of Michigan Ann ArborMI48109 United States Department of Internal Medicine Division of Pulmonary and Critical Care Medicine University of Michigan Ann ArborMI48109 United States Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States Weil Institute for Critical Care Research and Innovation University of Michigan Ann ArborMI48109 United States

The search for reliable protein biomarker candidates is critical for early disease detection and treatment. However, current immunoassay technologies are failing to meet increasing demands for sensitivity and multiplexing. Here, the authors have created a highly sensitive protein microarray using the principle of single-molecule counting for signal amplification, capable of simultaneously detecting a panel of cancer biomarkers at sub-pg/mL levels. To enable this amplification strategy, the authors introduce a novel method of protein patterning using photolithography to subdivide addressable arrays of capture antibody spots into hundreds of thousands of individual microwells. This allows for the total sensor area to be miniaturized, increasing the total possible multiplex capacity. With the immunoassay realized on a standard 75x25 mm form factor glass substrate, sample volume consumption is minimized to © 2023, The Authors. All rights reserved.

关键词： Biomarkers

Realistic ultrasound image synthesis for improved classification of liver disease

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arXiv 2021年

作者： Che, Hui Ramanathan, Sumana Foran, David J. Nosher, John L. Patel, Vishal M. Hacihaliloglu, Ilker Department of Biomedical Engineering Rutgers University NJ United States Department of Radiology Rutgers Robert Wood Johnson Medical School NJ United States Department of Electrical and Computer Engineering Johns Hopkins University MD United States Rutgers Cancer Institute of New Jersey NJ United States

With the success of deep learning-based methods applied in medical image analysis, convolutional neural networks (CNNs) have been investigated for classifying liver disease from ultrasound (US) data. However, the scarcity of available large-scale labeled US data has hindered the success of CNNs for classifying liver disease from US data. In this work, we propose a novel generative adversarial network (GAN) architecture for realistic diseased and healthy liver US image synthesis. We adopt the concept of stacking to synthesize realistic liver US data. Quantitative and qualitative evaluation is performed on 550 in-vivo B-mode liver US images collected from 55 subjects. We also show that the synthesized images, together with real in vivo data, can be used to significantly improve the performance of traditional CNN architectures for Nonalcoholic fatty liver disease (NAFLD) classification. © 2021, CC BY-NC-ND.

关键词： Generative adversarial networks