keyword
https://read.qxmd.com/read/38475138/gpu-based-parallel-processing-techniques-for-enhanced-brain-magnetic-resonance-imaging-analysis-a-review-of-recent-advances
#1
REVIEW
Ayca Kirimtat, Ondrej Krejcar
The approach of using more than one processor to compute in order to overcome the complexity of different medical imaging methods that make up an overall job is known as GPU (graphic processing unit)-based parallel processing. It is extremely important for several medical imaging techniques such as image classification, object detection, image segmentation, registration, and content-based image retrieval, since the GPU-based parallel processing approach allows for time-efficient computation by a software, allowing multiple computations to be completed at once...
February 29, 2024: Sensors
https://read.qxmd.com/read/38473581/effect-of-steel-fibers-on-tensile-properties-of-ultra-high-performance-concrete-a-review
#2
REVIEW
Wanghui Du, Feng Yu, Liangsheng Qiu, Yixuan Guo, Jialiang Wang, Baoguo Han
Ultra-high-performance concrete (UHPC) is an advanced cement-based material with excellent mechanical properties and durability. However, with the improvement of UHPC's compressive properties, its insufficient tensile properties have gradually attracted attention. This paper reviews the tensile properties of steel fibers in UHPC. The purpose is to summarize the existing research and to provide guidance for future research. The relevant papers were retrieved through three commonly used experimental methods for UHPC tensile properties (the direct tensile test, flexural test, and splitting test), and classified according to the content, length, type, and combination of the steel fibers...
February 28, 2024: Materials
https://read.qxmd.com/read/38436836/echoes-of-images-multi-loss-network-for-image-retrieval-in-vision-transformers
#3
JOURNAL ARTICLE
Anshul Pundhir, Shivam Sagar, Pradeep Singh, Balasubramanian Raman
This paper introduces a novel approach to enhance content-based image retrieval, validated on two benchmark datasets: ISIC-2017 and ISIC-2018. These datasets comprise skin lesion images that are crucial for innovations in skin cancer diagnosis and treatment. We advocate the use of pre-trained Vision Transformer (ViT), a relatively uncharted concept in the realm of image retrieval, particularly in medical scenarios. In contrast to the traditionally employed Convolutional Neural Networks (CNNs), our findings suggest that ViT offers a more comprehensive understanding of the image context, essential in medical imaging...
March 4, 2024: Medical & Biological Engineering & Computing
https://read.qxmd.com/read/38403628/evaluation-of-retrieval-accuracy-and-visual-similarity-in-content-based-image-retrieval-of-chest-ct-for-obstructive-lung-disease
#4
JOURNAL ARTICLE
Jooae Choe, Hye Young Choi, Sang Min Lee, Sang Young Oh, Hye Jeon Hwang, Namkug Kim, Jihye Yun, Jae Seung Lee, Yeon-Mok Oh, Donghoon Yu, Byeongsoo Kim, Joon Beom Seo
The aim of our study was to assess the performance of content-based image retrieval (CBIR) for similar chest computed tomography (CT) in obstructive lung disease. This retrospective study included patients with obstructive lung disease who underwent volumetric chest CT scans. The CBIR database included 600 chest CT scans from 541 patients. To assess the system performance, follow-up chest CT scans of 50 patients were evaluated as query cases, which showed the stability of the CT findings between baseline and follow-up chest CT, as confirmed by thoracic radiologists...
February 26, 2024: Scientific Reports
https://read.qxmd.com/read/38395957/a-deep-learning-dataset-for-sample-preparation-artefacts-detection-in-multispectral-high-content-microscopy
#5
JOURNAL ARTICLE
Vaibhav Sharma, Artur Yakimovich
High-content image-based screening is widely used in Drug Discovery and Systems Biology. However, sample preparation artefacts may significantly deteriorate the quality of image-based screening assays. While detection and circumvention of such artefacts could be addressed using modern-day machine learning and deep learning algorithms, this is widely impeded by the lack of suitable datasets. To address this, here we present a purpose-created open dataset of high-content microscopy sample preparation artefact...
February 23, 2024: Scientific Data
https://read.qxmd.com/read/38370630/protocol-for-cerebellar-stimulation-for-aphasia-rehabilitation-cesar-a-randomized-double-blind-sham-controlled-trial
#6
Becky Lammers, Myra J Sydnor, Sarah Cust, Ji Hyun Kim, Gayane Yenokyan, Argye E Hillis, Rajani Sebastian
UNLABELLED: In this randomized, double-blind, sham-controlled trial of Cerebellar Stimulation for Aphasia Rehabilitation (CeSAR), we will determine the effectiveness of cathodal tDCS (transcranial direct current stimulation) to the right cerebellum for the treatment of chronic aphasia (>6 months post stroke). We will test the hypothesis that cerebellar tDCS in combination with an evidenced-based anomia treatment (semantic feature analysis, SFA) will be associated with greater improvement in naming untrained pictures (as measured by the change in Philadelphia Picture Naming Test), 1-week post treatment, compared to sham plus SFA...
February 6, 2024: medRxiv
https://read.qxmd.com/read/38322427/an-intelligent-search-retrieval-system-iris-and-clinical-and-research-repository-for-decision-support-based-on-machine-learning-and-joint-kernel-based-supervised-hashing
#7
JOURNAL ARTICLE
David J Foran, Wenjin Chen, Tahsin Kurc, Rajarshi Gupta, Jakub Roman Kaczmarzyk, Luke Austin Torre-Healy, Erich Bremer, Samuel Ajjarapu, Nhan Do, Gerald Harris, Antoinette Stroup, Eric Durbin, Joel H Saltz
Large-scale, multi-site collaboration is becoming indispensable for a wide range of research and clinical activities in oncology. To facilitate the next generation of advances in cancer biology, precision oncology and the population sciences it will be necessary to develop and implement data management and analytic tools that empower investigators to reliably and objectively detect, characterize and chronicle the phenotypic and genomic changes that occur during the transformation from the benign to cancerous state and throughout the course of disease progression...
2024: Cancer Informatics
https://read.qxmd.com/read/38306338/block-mapping-and-dual-matrix-based-watermarking-for-image-authentication-with-self-recovery-capability
#8
JOURNAL ARTICLE
Xuejing Li, Qiancheng Chen, Runfu Chu, Wei Wang
Numerous image authentication techniques have been devised to address the potential security issue of malicious tampering with image content since digital images can be easily duplicated, modified, transformed and diffused via the Internet transmission. However, the existing works still remain many shortcomings in terms of the recovery incapability and detection accuracy with extensive tampering. To improve the performance of tamper detection and image recovery, we present a block mapping and dual-matrix-based watermarking scheme for image authentication with self-recovery capability in this paper...
2024: PloS One
https://read.qxmd.com/read/38271352/detecting-microsatellite-instability-in-colorectal-cancer-using-transformer-based-colonoscopy-image-classification-and-retrieval
#9
JOURNAL ARTICLE
Chung-Ming Lo, Jeng-Kai Jiang, Chun-Chi Lin
Colorectal cancer (CRC) is a major global health concern, with microsatellite instability-high (MSI-H) being a defining characteristic of hereditary nonpolyposis colorectal cancer syndrome and affecting 15% of sporadic CRCs. Tumors with MSI-H have unique features and better prognosis compared to MSI-L and microsatellite stable (MSS) tumors. This study proposed establishing a MSI prediction model using more available and low-cost colonoscopy images instead of histopathology. The experiment utilized a database of 427 MSI-H and 1590 MSS colonoscopy images and vision Transformer (ViT) with different feature training approaches to establish the MSI prediction model...
2024: PloS One
https://read.qxmd.com/read/38232396/interactive-content-based-image-retrieval-with-deep-learning-for-ct-abdominal-organ-recognition
#10
JOURNAL ARTICLE
Chung-Ming Lo, Chi-Cheng Wang, Peng-Hsiang Hung
Recognizing the most relevant seven organs in an abdominal computed tomography (CT) slice requires sophisticated knowledge. This study proposed automatically extracting relevant features and applying them in a content-based image retrieval (CBIR) system to provide similar evidence for clinical use.
Approach: A total of 2827 abdominal CT slices, including 638 liver, 450 stomach, 229 pancreas, 442 spleen, 362 right kidney, 424 left kidney and 282 gallbladder tissues, were collected to evaluate the proposed CBIR in the present study...
January 17, 2024: Physics in Medicine and Biology
https://read.qxmd.com/read/38224614/self-attention-driven-retrieval-of-chest-ct-images-for-covid-19-assessment
#11
JOURNAL ARTICLE
Victoria Fili, Michalis Savelonas
Numerous methods have been developed for computer-aided diagnosis (CAD) of coronavirus disease-19 (COVID-19), based on chest computed tomography (CT) images. The majority of these methods are based on deep neural networks and often act as "black boxes" that cannot easily gain the trust of medical community, whereas their result is uniformly influenced by all image regions. This work introduces a novel, self-attention-driven method for content-based image retrieval (CBIR) of chest CT images. The proposed method analyzes a query CT image and returns a classification result, as well as a list of classified images, ranked according to similarity with the query...
January 15, 2024: Biomedical Physics & Engineering Express
https://read.qxmd.com/read/38157546/intensive-vision-guided-network-for-radiology-report-generation
#12
JOURNAL ARTICLE
Fudan Zheng, Mengfei Li, Ying Wang, Weijiang Yu, Ruixuan Wang, Zhiguang Chen, Nong Xiao, Yutong Lu
Automatic radiology report generation is booming due to its huge application potential for the healthcare industry. However, existing computer vision and natural language processing approaches to tackle this problem are limited in two aspects. First, when extracting image features, most of them neglect multi-view reasoning in vision and model single-view structure of medical images, such as space-view or channel-view. However, clinicians rely on multi-view imaging information for comprehensive judgment in daily clinical diagnosis...
December 29, 2023: Physics in Medicine and Biology
https://read.qxmd.com/read/38132695/efficient-retrieval-of-images-with-irregular-patterns-using-morphological-image-analysis-applications-to-industrial-and-healthcare-datasets
#13
JOURNAL ARTICLE
Jiajun Zhang, Georgina Cosma, Sarah Bugby, Jason Watkins
Image retrieval is the process of searching and retrieving images from a datastore based on their visual content and features. Recently, much attention has been directed towards the retrieval of irregular patterns within industrial or healthcare images by extracting features from the images, such as deep features, colour-based features, shape-based features, and local features. This has applications across a spectrum of industries, including fault inspection, disease diagnosis, and maintenance prediction. This paper proposes an image retrieval framework to search for images containing similar irregular patterns by extracting a set of morphological features (DefChars) from images...
December 13, 2023: Journal of Imaging
https://read.qxmd.com/read/38124593/a-review-of-fine-grained-sketch-image-retrieval-based-on-deep-learning
#14
JOURNAL ARTICLE
Qing Luo, Xiang Gao, Bo Jiang, Xueting Yan, Wanyuan Liu, Junchao Ge
Sketch image retrieval is an important branch of the image retrieval field, mainly relying on sketch images as queries for content search. The acquisition process of sketch images is relatively simple and in some scenarios, such as when it is impossible to obtain photos of real objects, it demonstrates its unique practical application value, attracting the attention of many researchers. Furthermore, traditional generalized sketch image retrieval has its limitations when it comes to practical applications; merely retrieving images from the same category may not adequately identify the specific target that the user desires...
November 28, 2023: Mathematical Biosciences and Engineering: MBE
https://read.qxmd.com/read/38104401/sketch-based-semantic-retrieval-of-medical-images
#15
JOURNAL ARTICLE
Kazuma Kobayashi, Lin Gu, Ryuichiro Hataya, Takaaki Mizuno, Mototaka Miyake, Hirokazu Watanabe, Masamichi Takahashi, Yasuyuki Takamizawa, Yukihiro Yoshida, Satoshi Nakamura, Nobuji Kouno, Amina Bolatkan, Yusuke Kurose, Tatsuya Harada, Ryuji Hamamoto
The volume of medical images stored in hospitals is rapidly increasing; however, the utilization of these accumulated medical images remains limited. Existing content-based medical image retrieval (CBMIR) systems typically require example images, leading to practical limitations, such as the lack of customizable, fine-grained image retrieval, the inability to search without example images, and difficulty in retrieving rare cases. In this paper, we introduce a sketch-based medical image retrieval (SBMIR) system that enables users to find images of interest without the need for example images...
December 8, 2023: Medical Image Analysis
https://read.qxmd.com/read/37896519/a-short-video-classification-framework-based-on-cross-modal-fusion
#16
JOURNAL ARTICLE
Nuo Pang, Songlin Guo, Ming Yan, Chien Aun Chan
The explosive growth of online short videos has brought great challenges to the efficient management of video content classification, retrieval, and recommendation. Video features for video management can be extracted from video image frames by various algorithms, and they have been proven to be effective in the video classification of sensor systems. However, frame-by-frame processing of video image frames not only requires huge computing power, but also classification algorithms based on a single modality of video features cannot meet the accuracy requirements in specific scenarios...
October 12, 2023: Sensors
https://read.qxmd.com/read/37892874/wwfedcbmir-world-wide-federated-content-based-medical-image-retrieval
#17
JOURNAL ARTICLE
Zahra Tabatabaei, Yuandou Wang, Adrián Colomer, Javier Oliver Moll, Zhiming Zhao, Valery Naranjo
The paper proposes a federated content-based medical image retrieval (FedCBMIR) tool that utilizes federated learning (FL) to address the challenges of acquiring a diverse medical data set for training CBMIR models. CBMIR is a tool to find the most similar cases in the data set to assist pathologists. Training such a tool necessitates a pool of whole-slide images (WSIs) to train the feature extractor (FE) to extract an optimal embedding vector. The strict regulations surrounding data sharing in hospitals makes it difficult to collect a rich data set...
September 28, 2023: Bioengineering
https://read.qxmd.com/read/37801949/estimation-of-the-distribution-patterns-of-heavy-metal-in-soil-from-airborne-hyperspectral-imagery-based-on-spectral-absorption-characteristics
#18
JOURNAL ARTICLE
Kun Tan, Lihan Chen, Huimin Wang, Zhaoxian Liu, Jianwei Ding, Xue Wang
Though soil is widely known as one of the most valuable resources for the world, its quality is going to be lower because of unsustainable economic development and social progress. Therefore, it is important for us to monitor and evaluate the quality of soil, especially its heavy metal contents which is too scarce to identify in soil spectra easily but poisonous enough to affect human health in a long run. Most of the existing estimation methods have based the characteristic bands on statistical analysis to a large extent, which is hard to accurately explain the retrieval mechanism...
October 4, 2023: Journal of Environmental Management
https://read.qxmd.com/read/37789095/improving-diagnosis-accuracy-with-an-intelligent-image-retrieval-system-for-lung-pathologies-detection-a-features-extractor-approach
#19
JOURNAL ARTICLE
Abdelbaki Souid, Najah Alsubaie, Ben Othman Soufiene, Mohammed S Alqahtani, Mohamed Abbas, Layal K Jambi, Hedi Sakli
Detecting lung pathologies is critical for precise medical diagnosis. In the realm of diagnostic methods, various approaches, including imaging tests, physical examinations, and laboratory tests, contribute to this process. Of particular note, imaging techniques like X-rays, CT scans, and MRI scans play a pivotal role in identifying lung pathologies with their non-invasive insights. Deep learning, a subset of artificial intelligence, holds significant promise in revolutionizing the detection and diagnosis of lung pathologies...
October 3, 2023: Scientific Reports
https://read.qxmd.com/read/37785473/identifying-common-topics-in-patient-portal-messages-with-unsupervised-natural-language-processing
#20
JOURNAL ARTICLE
J H Chang, A Lin, L Singer, O Mohamad, J Chan, I Friesner, T Zack, A Ashraf-Ganjouei, L Boreta, A Gottschalk, S E Braunstein, C C Park, J C Hong
PURPOSE/OBJECTIVE(S): Patient portal messaging is an increasingly important form of communication between patients and medical providers. This has become particularly relevant in oncology, where patients undergo intense longitudinal treatments that require frequent communication regarding symptoms, appointments, and diagnostic results. The rise in the volume of these messages has significantly increased the workload of medical providers and consequent physician burn-out. Natural language processing (NLP), particularly transformer-based models, may offer an automated approach to characterize the content of patient messages and improve message triage and routing...
October 1, 2023: International Journal of Radiation Oncology, Biology, Physics
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