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A Multi-phase Deep Learning Approach for Predicting HCC Response to TACE using Complete Computed Tomography

A Multi-phase Deep Learning Approach for Predicting HCC Resp...

2023 IEEE EMBS Special Topic Conference on Data science and engineering in Healthcare, Medicine and Biology, IEEECONF 2023

作者： Chang, Shun Cheng Liu, Chien Fu Misztal, Marta Hsieh, Yi Hsien Lin, Che Taiwan School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Department of Electrical Engineering Ntu Taiwan Center for Computational and Systems Biology Center for Biotechnology Ntu Taiwan Smart Medicine and Health Informatics Program Ntu Taiwan

ISBN: (纸本)9798350383386

In this research study, we compare the predictive performance of two advanced deep learning-based models in order to provide a solution to TACE (Transarterial Chemoembolization) response prediction in HCC (Hepatocellular Carcinoma) patients. Using entire abdominal CT scans enabled a broader perspective available for the model, eliminating the need for segmentation during the preprocessing. Making use of both single-phase and multi-phase CT imaging, we have used DenseNet121 and have obtained an accuracy of 80% for the multi-phase *** Relevance: The ability to predict the effectiveness of TACE treatment prior to its administration makes it possible to provide a better decision-making aid for physicians and patients. © 2023 IEEE.

关键词： computerized tomography

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Understanding the stability of advanced inorganic-organic hybrid thin films for advanced resist applications 41

Understanding the stability of advanced inorganic-organic hy...

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Advances in Patterning Materials and Processes XLI 2024

作者： Le, Dan N. Chu, Thi Thu Huong Kim, Jin-Hyun Veyan, Jean-Francois Lee, Won-Il Tiwale, Nikhil Lee, Minjong Choi, Seungsoo Woo, Jihoon Nam, Chang-Yong Choi, Rino Kim, Jiyoung Department of Materials Science and Engineering University of Texas at Dallas RichardsonTX75080 United States Department of Materials Science and Chemical Engineering Stony Brook University Stony BrookNY11794 United States Center for Functional Nanomaterials Brookhaven National Laboratory UptonNY11973 United States Department of Electrical and Computer Engineering University of Texas at Dallas RichardsonTX75080 United States Program in Enviromental and Polymer Engineering Inha University Incheon22212 Korea Republic of Department of Materials Science and Engineering Inha University Incheon22212 Korea Republic of

ISBN: (纸本)9781510672208

In this study, we focus on examining the stability of Al-based inorganic-organic hybrid thin films deposited through the molecular atomic layer deposition (MALD) process in ambient environment. Our observations reveal an initial reduction in material thickness within the first 3 days, followed by a period of stability. XPS analysis is employed to further investigate the chemical alternations in the aged Al-based hybrid thin films, revealing an increase in C=O species as well as overall oxygen content in the material. We also evaluate the effects of atmospheric exposure on the sensitivity of the Al-based hybrid thin films using electron flood exposure. This study aims to enhance the understanding of the stability of a vapor-phase synthesized hybrid thin film system for advanced resist applications. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Atomic layer deposition

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Impact of In flux on self-assembled InGaN/GaN superlattice grown on GaN template by plasma-assisted molecular beam epitaxy

Impact of In flux on self-assembled InGaN/GaN superlattice g...

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2022 Compound Semiconductor Week, CSW 2022

作者： Khan, Kamruzzaman Sun, Kai Wurm, Christian Ahmadi, Elaheh University of Michigan Department of Materials Science and Engineering Ann ArborMI United States University of California Department of Electrical and Computer Engineering Santa BarbaraCA United States University of Michigan Department of Electrical Engineering and Computer Science Ann ArborMI United States University of Michigan Applied Physics Program Ann ArborMI United States

ISBN: (数字)9781665453400

ISBN: (纸本)9781665453400

We have previously reported spontanous formation of InGaN/GaN superlattice structure on nominal InGaN films grown by plasma-assisted molecular beam epitaxy (PAMBE). In this work, we report on the impact of In flux on the formation and periodicity of self-assembled InxGa1-xN/InyGa1-yN superlattice structure (SASL). We show that the thickness and In composition in the InGaN layer varies by changing the In flux. These films were stucturally characterized by X-ray diffraction and simulated by globalfit software to get the thickness and In composition. The superlattice structures were confimred by scanning transmission electron microscopy and the thickness of InGaN layer and In composition were determined by energy dispersive X-ray spectroscopy. This work can provide a method for using the SASL with better control for their optoelectronics application. © 2022 IEEE.

关键词： III-V semiconductors

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ConvNeXt Model for Breast Cancer Image Classification

ConvNeXt Model for Breast Cancer Image Classification

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Devin Setiawan Andrea Stevens Karnyoto Indo Intan Bens Pardamean Department of Electrical Engineering and Computer Science University of Kansas Lawrence United States Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Department of Informatics Engineering Universitas Dipa Makassar Makassar Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331539603

ISBN: (纸本)9798331539610

Breast cancer is an occurrence of cancer that attacks breast tissue and is the most common cancer among women worldwide, affecting one in eight women. In this modern world, breast cancer image classification simplifies the process of analyzing, providing objective and accurate results. By leveraging machine learning algorithms and computer vision techniques, we developed breast cancer detection. The dataset is histopathology dataset from BreakHis and UNHAS Hospital. We chose the ConvNeXt-Tiny model then modified the classifier head as the proposed method. Before the dataset is processed by the model, we augment the images by applying random horizontal and vertical flips, adjustments to brightness, contrast, saturation, and hue using color jitter. The augmentation process simulates real-world variance and enhances the model's ability to generalize to unseen data. Our proposed model gained better performance (accuracy, F1-Score) results compared two other techniques to VGG16 and SVM. According to our experiments, the F1-Score for the ConvNeXt-Tiny model with classifier head modification is higher at 0.9516, than the gain for VGG16 at 0.9292, and the gain for the SVM at 0.83.

关键词： Support vector machines Accuracy Head Computational modeling Transfer learning Breast cancer Magnetic heads Data models Reliability Image classification

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Expanding the Surgical Robotics Community: An Intuitive Sim-to-Real Control Framework for Raven-II with a Budget-Friendly Gamepad Controller

Expanding the Surgical Robotics Community: An Intuitive Sim-...

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International Symposium on Medical Robotics (ISMR)

作者： Mai Bui Natalie Chalfant Cuiling Sun Sean Fabrega Haonan Peng Kevin Huang Yun-Hsuan Su Department of Computer Science Mount Holyoke College South Hadley MA Department of Electrical and Computer Engineering University of Washington Seattle WA Picker Engineering Program Smith College Northampton MA

ISBN: (数字)9798350377118

ISBN: (纸本)9798350377125

This paper proposes a low-cost interface and refined digital twin for the Raven-II surgical robot. Previous simulations of the Raven-II, e.g. via the Asynchronous Multibody Framework (AMBF), presented salient drawbacks, including control inputs inconsistent with Raven-II software, and lack of stable, high-fidelity physical contact simulations. This work bridges both of these gaps, both (1) enabling robust, simulated contact mechanics for dynamic physical interactions with the Raven-II, and (2) developing a universal input format for both simulated and physical platforms. The method furthermore proposes a low cost, commodity game-controller interface for controlling both virtual and real realizations of Raven-II, thus greatly reducing the barrier to access for Raven-II research and collaboration. Overall, this work aims to eliminate the inconsistencies between simulated and real representations of the Raven-II. Such a development can expand the reach of surgical robotics research. Namely, providing end-to-end transparency between the simulated AMBF and physical Raven-II platforms enables a software testbed previously unavailable, e.g. for training real surgeons, for creating digital synthetic datasets, or for prototyping novel architectures like shared control strategies. Experiments validate this transparency by comparing joint trajectories between digital twin and physical testbed given identical inputs. This work may be extended and incorporated into recent efforts in developing modular or common software infrastructures for both simulation and control of real robotic devices, such as the Collaborative Robotics Toolkit (CRTK).

关键词： Training Medical robotics Costs Collaboration Surgery computer architecture Software

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GANscan:continuous scanning microscopy using deep learning deblurring

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Light(science & Applications) 2022年第10期11卷 2378-2387页

作者： Michael John Fanous Gabriel Popescu Quantitative Light Imaging Laboratory Beckman Institute for Advanced Science and TechnologyUniversity of Illinois at Urbana-ChampaignUrbanaIL 61801USA Department of Bioengineering University of Illinois at Urbana-Champaign306 N.Wright StreetUrbanaIL 61801USA Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign306 N.Wright StreetUrbanaIL 61801USA

Most whole slide imaging(WSI)systems today rely on the"stop-and-stare"approach,where,at each field of view,the scanning stage is brought to a complete stop before the camera snaps a *** procedure ensures that each image is free of motion blur,which comes at the expense of long acquisition *** order to speed up the acquisition process,especially for large scanning areas,such as pathology slides,we developed an acquisition method in which the data is acquired continuously while the stage is moving at high *** generative adversarial networks(GANs),we demonstrate this ultra-fast imaging approach,referred to as GANscan,which restores sharp images from motion blurred *** allows us to complete image acquisitions at 30x the throughput of stop-and-stare *** method is implemented on a Zeiss Axio Observer Z1 microscope,requires no specialized hardware,and accomplishes successful reconstructions at stage speeds of up to 5000 μm/*** validate the proposed method by imaging H&E stained tissue *** method not only retrieves crisp images from fast,continuous scans,but also adjusts for defocusing that occurs during scanning within+/-5 μ*** a consumer GPU,the inference runs at<20 ms/image.

关键词： hardware continuous image

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Prediction of DHF (Dengue Hemorrhagic Fever) Severity Using Random Forest, KNN, Decision Tree and Naïve Bayes 7

Prediction of DHF (Dengue Hemorrhagic Fever) Severity Using ...

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7th IEEE International Conference on Information Technology and Digital Applications, ICITDA 2022

作者： Pohan, Husni Iskandar Suparta, Wayan Heryadi, Yaya Wibowo, Antoni Lukas Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia Institut Teknologi Nasional Yogyakarta Department of Electrical Engineering Yogyakarta55281 Indonesia Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Jakarta12930 Indonesia

ISBN: (数字)9781665461368

ISBN: (纸本)9781665461368

Dengue Hemorrhagic Fever is an acute viral infectious disease caused by the dengue virus. Transmitted through the bite of Aedes Mosquitoes and divided into 4 severity. Severity 1 and 2 are characterized by a decrease in platelet levels below normal and there is a change in hemoconcentration, while Severity 3 and 4 are included in Dengue Shock Syndrome (DSS). The purpose of the study was to classify the severity of dengue, based on age, gender, platelets, body temperature, bleeding, and also doctor's recommendation. The advancement of information technology is the reason for the development of medical science, especially with artificial intelligence. Abundant medical data becomes a source that can be processed in improving the quality of medical information. The method chosen is Random Forest, kNN, Decision Tree and Naïve Bayes. The dataset used is the medical records of all DHF patients for 30 months in the period Jan 2019 to June 2021, at the Nadhifa Al Ghiffari Clinic in Bojongsoang District, Bandung Regency. © 2022 IEEE.

关键词： Decision trees

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Automated Segmentation of Lung Regions in 3D CT Scans Using Hybrid Unsupervised-Supervised Models

Automated Segmentation of Lung Regions in 3D CT Scans Using ...

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IEEE International Conference on Image Processing

作者： Ahmed Sharafeldeen Adel Khelifi Mohammed Ghazal Maha Yaghi Sohail Contractor Ayman El-Baz Department of Bioengineering University of Louisville USA Computer Science and Information Technology Department Abu Dhabi University UAE Electrical Computer and Biomedical Engineering Department Abu Dhabi University UAE Department of Radiology University of Louisville USA

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

This paper introduces an automatic segmentation system designed for precise outlining of the pulmonary area within 3D computed tomography (CT) scans, utilizing a combination of unsupervised and supervised models. Initially, an unsupervised model is utilized to depict the empirical distribution of Hounsfield units in both lung and chest regions within the 3D CT volume. This representation takes the form of a probability model based on a linear combination of Gaussians, determined through a modified expectation maximization (EM) algorithm. Subsequently, the LCG-based segmentation is refined by modeling it with a spatial probabilistic model using a 3D Markov Gibbs random field (MGRF) with analytically estimated potentials. Finally, a supervised deep learning model is introduced and integrated with the proposed unsupervised model to achieve superior segmentation results. The efficacy of the proposed method is assessed using 3D chest scans from 29 patients confirmed with varying degrees of severity in COVID-19. This evaluation employs four distinct metrics: Dice similarity coefficient (DSC), overlap coefficient, Hausdorff distance (HD), and absolute volume difference (AVD), achieving remarkable results of $97.35_{ \pm 1.51} \%, 94.89_{ \pm 2.80} \%$, $3.39_{ \pm 1.61}$, and $2.70_{ \pm 2.88}$, respectively. When compared to four state-of-the-art deep learning-based methods, the proposed system demonstrated outstanding performance in segmenting pathological lung tissues, highlighting its potential and efficacy.

关键词： Deep learning Solid modeling Image segmentation Analytical models Three-dimensional displays Shape Computed tomography

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Ionic Crosslinked Biopolymer-Ceramic Beads for Bone Tissue engineering 6th

Ionic Crosslinked Biopolymer-Ceramic Beads for Bone Tissue E...

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6th International Conference on Nanotechnologies and Biomedical engineering, ICNBME 2023

作者： Cojocaru, Florina Daniela Toader, Claudia Valentina Dodi, Gianina Gardikiotis, Ioannis Calistru, Anca Elena Rotaru, Aurelian Balan, Vera Verestiuc, Liliana Biomedical Sciences Department Faculty of Medical Bioengineering Grigore T. Popa University of Medicine and Pharmacy of Iasi Iasi Romania Grigore T. Popa University of Medicine and Pharmacy of Iasi Iasi Romania Department of Pedotechnics Iasi University of Life Sciences Iasi Romania Department of Electrical Engineering and Computer Science Stefan cel Mare University of Suceava Suceava Romania

ISBN: (纸本)9783031427749

In several situation when bone integrity is prejudiced, advanced regenerative medicine approaches are involved in order to get a good result, being designed and applied synthetic tissue engineered architectures. This rapidly evolving interdisciplinary domain, tissue engineering, is centered on developing three-dimensional scaffolds, which can be prepared from ceramics, polymers or the combination between those two, resulting a complex material mimicking the composition of the natural bone. Among polymers, polysaccharides are a remarkable class, due to the inexhaustible source, great biocompatibility and versatility in terms of processability. Alginate and guar gum, included in this class have the ability to crosslink in the presence of Ca2+ ions, resulting easy to handle beads. Due to the similarity with human inorganic matter, calcium phosphates are used in orthopedy, frequently combined with polymers to overcome one of the main disadvantages of ceramics: brittleness, which negatively influences mechanical behavior. All these aspects being considered, the aim of the study was to obtain alginate–calcium phosphate beads with inclusion of carboxymethyl guar gum nanoparticles as scaffolds for bone tissue engineering and analyze them in terms of morphology and composition (Field Emission Scanning Electron Microscope and Energy-dispersive X-ray Spectrometer), chemical structure (Fourier Transform Infrared Spectrometer) and behavior in simulated body fluids. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Calcium phosphate

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Cascaded multiparameter quantum metrology

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Physical Review A 2025年第1期111卷 012618-012618页

作者： Gregory Krueper Lior Cohen Juliet T. Gopinath Department of Physics University of Colorado Boulder Boulder Colorado 80309 USA Department of Electrical Computer and Energy Engineering University of Colorado Boulder Boulder Colorado 80309 USA Materials Science and Engineering Program University of Colorado Boulder Boulder Colorado 80309 USA

We present an innovative, platform-independent concept for multiparameter sensing where the measurable parameters are in series, or cascaded, enabling measurements as a function of position. With temporally resolved detection, we show that squeezing can give a quantum enhancement in sensitivity over that of classical states by a factor of e2r, where r≈1 is the squeezing parameter. As an example, we have modeled an interferometer that senses multiple phase shifts along the same path, demonstrating a maximal quantum advantage by combining a coherent state with squeezed vacuum. Further classical modeling with up to 100 phases shows linear scaling potential for adding nodes to the sensor. The approach can be applied to remote sensing, geophysical surveying, and infrastructure monitoring.

关键词： Interferometers

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