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Multi-Label Learning With Visual-Semantic Embedded Knowledge Graph for Diagnosis of Radiology Imaging

Daibing Hou, Zijian Zhao, Sanyuan Hu
2021 IEEE Access  
A significant task of automatic diagnosis for radiology imaging, especially for chest X-rays, is to identify disease types, which can be viewed as a multi-label learning problem.  ...  However, the utilization of medical reports paired with radiology images is neglected in such approaches. Hence, at least two novel improvements are proposed in this paper.  ...  The authors of DenseNet-KG constructed a medical concept graph based on prior knowledge to diagnose chest-X-ray images.  ... 
doi:10.1109/access.2021.3052794 fatcat:y7nddtvezbehpof6uiws73oxki

Retrieval From and Understanding of Large-Scale Multi-modal Medical Datasets: A Review

Henning Muller, Devrim Unay
2017 IEEE transactions on multimedia  
This text is a systematic review of recent work (concentrating on the period between 2011-2017) on content-based multi-modal retrieval and image understanding in the medical domain, where image understanding  ...  based on the experience available in the multimedia community.  ...  related radiology reports for multimodal retrieval enriched by semantics.  ... 
doi:10.1109/tmm.2017.2729400 fatcat:td4s7hbegzbmhlosalzlc3p7tq

Chest ImaGenome Dataset for Clinical Reasoning [article]

Joy T. Wu, Nkechinyere N. Agu, Ismini Lourentzou, Arjun Sharma, Joseph A. Paguio, Jasper S. Yao, Edward C. Dee, William Mitchell, Satyananda Kashyap, Andrea Giovannini, Leo A. Celi, Mehdi Moradi
2021 arXiv   pre-print
extracted from text reports, or trained via a joint image and unstructured text learning strategy.  ...  Local annotations are automatically produced using a joint rule-based natural language processing (NLP) and atlas-based bounding box detection pipeline.  ...  Acknowledgements This work was supported by the Rensselaer-IBM AI Research Collaboration, part of the IBM AI Horizons Network, and the IBM-MIT Critical Data Collaboration.  ... 
arXiv:2108.00316v1 fatcat:lfuj6nv2znghdd2vwnfljqee6e

Automatic Report Generation for Chest X-Ray Images via Adversarial Reinforcement Learning

Daibing Hou, Zijian Zhao, Yuying Liu, Faliang Chang, Sanyuan Hu
2021 IEEE Access  
INTRODUCTION Automatic radiology-report generation is a computer-aided diagnostic technology used for generating a free-text description of disease diagnosis or future treatment based on radiology images  ...  [5] conducted a multi-view fusion on deep features after the CNN backbone.  ... 
doi:10.1109/access.2021.3056175 fatcat:ryv4noypxbfhdeadytlme26wgq

Towards case-based medical learning in radiological decision making using content-based image retrieval

Petra Welter, Thomas M Deserno, Benedikt Fischer, Rolf W Günther, Cord Spreckelsen
2011 BMC Medical Informatics and Decision Making  
To overcome the known drawbacks of existing learning systems, we developed the concept of image-based case retrieval for radiological education (IBCR-RE).  ...  Radiologists' training is based on intensive practice and can be improved with the use of diagnostic training systems.  ...  The IRMAdiag trainer is based on approved learning methods applied in both protected and realistic contexts and represents a modern training concept to enrich the range of current medical training.  ... 
doi:10.1186/1472-6947-11-68 pmid:22032775 pmcid:PMC3217894 fatcat:xt6e2bzelvfqdgvpi5b465bdga

A survey on attention mechanisms for medical applications: are we moving towards better algorithms? [article]

Tiago Gonçalves, Isabel Rio-Torto, Luís F. Teixeira, Jaime S. Cardoso
2022 arXiv   pre-print
on medical image classification with three different use cases.  ...  Naturally, the use of attention-based algorithms for medical applications occurred smoothly.  ...  Acknowledgements This work, developed within the scope of the project "TAMI -Transparent Artificial Medical Intelligence" (NORTE-01-0247-FEDER-045905), is co-financed by ERDF -European Regional Fund through  ... 
arXiv:2204.12406v1 fatcat:lwz3hvd44bfqnhf7n57ejehidu

Automatic Segmentation of Pelvic Cancers Using Deep Learning: State-of-the-Art Approaches and Challenges

Reza Kalantar, Gigin Lin, Jessica M. Winfield, Christina Messiou, Susan Lalondrelle, Matthew D. Blackledge, Dow-Mu Koh
2021 Diagnostics  
and rectal cancers on computed tomography (CT) and magnetic resonance imaging (MRI), highlighting the key findings, challenges and limitations.  ...  The recent rise of deep learning (DL) and its promising capabilities in capturing non-explicit detail from large datasets have attracted substantial research attention in the field of medical image processing  ...  Although previous publications have provided technical reviews of recent automatic medical image segmentation approaches, [12] [13] [14] [15] [16] [17] some with a particular focus on radiology [18]  ... 
doi:10.3390/diagnostics11111964 pmid:34829310 pmcid:PMC8625809 fatcat:alr36jtq6fgeddnluclp5neb2i

Prototypes for Content-Based Image Retrieval in Clinical Practice

Adrien Depeursinge
2011 Open Medical Informatics Journal  
We define applicability to clinical practice by having recently demonstrated the CBIR system on one of the CAD demonstration workshops held at international conferences, such as SPIE Medical Imaging, CARS  ...  Content-based image retrieval (CBIR) has been proposed as key technology for computer-aided diagnostics (CAD).  ...  hospital (WIDTH): interpretation, infrastructure, and integration).  ... 
doi:10.2174/1874431101105010058 pmid:21892374 pmcid:PMC3149811 fatcat:qf4z2x4ayffmbgd2wk72s3p7nq

Deformation and Refined Features Based Lesion Detection on Chest X-ray

Ce Li, Dong Zhang, Shaoyi Du, Zhiqiang Tian
2020 IEEE Access  
To deal with problems, we propose the deformation and refined features based lesion detection on the chest X-ray algorithm called DRCXNet.  ...  Automatic and accurate detection of chest X-ray lesion is a challenging task.  ...  [9] built a classifier based on PCA, linear SVM, and multi-kernel SVM, which can compare and discriminate between healthy controls (HC) and Alzheimer's disease (AD) patients.  ... 
doi:10.1109/access.2020.2963926 fatcat:fqbsoknsbbefpj5yv2fuwguxq4

Multispecialty Enterprise Imaging Workgroup Consensus on Interactive Multimedia Reporting Current State and Road to the Future: HIMSS-SIIM Collaborative White Paper

Christopher J. Roth, David A. Clunie, David J. Vining, Seth J. Berkowitz, Alejandro Berlin, Jean-Pierre Bissonnette, Shawn D. Clark, Toby C. Cornish, Monief Eid, Cree M. Gaskin, Alexander K. Goel, Genevieve C. Jacobs (+13 others)
2021 Journal of digital imaging  
, radiology, endoscopic procedural specialties, and other medical disciplines.  ...  The workgroup adopted a consensus definition of IMR as "interactive medical documentation that combines clinical images, videos, sound, imaging metadata, and/or image annotations with text, typographic  ...  Structured and synoptic data lend themselves far better than prose to generating interactive multimedia reports due to being able to automatically associate data element, images, and downstream actions  ... 
doi:10.1007/s10278-021-00450-5 pmid:34131793 pmcid:PMC8329131 fatcat:pi4r6xaajbfa3bovmahssoe4mi

Navigation in surgery

Uli Mezger, Claudia Jendrewski, Michael Bartels
2013 Langenbeck's archives of surgery (Print)  
Over the past decade, navigation in surgery has evolved beyond imaging modalities and bulky systems into the rich networking of the cloud or devices that are pocket-sized.  ...  Introduction "Navigation in surgery" spans a broad area, which, depending on the clinical challenge, can have different meanings.  ...  and the source are credited.  ... 
doi:10.1007/s00423-013-1059-4 pmid:23430289 pmcid:PMC3627858 fatcat:kzuvxitnxrgs3kaablcnkaar3u

Data Mining and Knowledge Discovery [chapter]

Krzysztof J. Cios, Witold Pedrycz, Roman W. Swiniarski
1998 Data Mining Methods for Knowledge Discovery  
In this paper, a review study is done on existing data mining and knowledge discovery techniques, applications and process models that are applicable to healthcare environments.  ...  Organizations that take advantage of KDD techniques will find that they can lower the healthcare costs while improving healthcare quality by using fast and better clinical decision making.  ...  images for radiology.  ... 
doi:10.1007/978-1-4615-5589-6_1 fatcat:p2oyvrwv6rdxlam7jtunbsz56q

Symmetry and asymmetry analysis and its implications to computer-aided diagnosis: A review of the literature

Sheena Xin Liu
2009 Journal of Biomedical Informatics  
lesions, based solely on the information contained in images.  ...  In neuro-imaging applications, for example, one way to perform this knowledge integration is to uncover symmetry/asymmetry information from the corresponding regions of the head and to explore its implication  ...  Celina Imielinska and Dr. Andrew Laine for the useful discussions concerning this topic.  ... 
doi:10.1016/j.jbi.2009.07.003 pmid:19615468 fatcat:kmwgz6btbncj5f6zomyiofohom

State-of-the-Art Mobile Radiation Detection Systems for Different Scenarios

Luís Marques, Alberto Vale, Pedro Vaz
2021 Sensors  
Examples of improvements are: the use of silicon photomultiplier-based scintillators, new scintillating crystals, compact dual-mode detectors (gamma/neutron), data fusion, mobile sensor networks, cooperative  ...  Four scenarios are considered: radiological and nuclear accidents and emergencies; illicit traffic of special nuclear materials and radioactive materials; nuclear, accelerator, targets, and irradiation  ...  its impact on the general public and environment radiation safety [38] and radiological risks.  ... 
doi:10.3390/s21041051 pmid:33557104 pmcid:PMC7913838 fatcat:b2bw2jhqifaadbwq5qkwkdgkjm


2015 Laboratory Investigation  
Background: The development of next generation sequencing (NGS) and associated target sequence enrichment technologies has enabled time and cost effective detection of clinically relevant molecular alterations  ...  efficient multi-step data processing.  ...  Whole slide imaging technology is used for the telepathology consultation service.  ... 
doi:10.1038/labinvest.2015.17 fatcat:74eygyy7o5gkrhizoootoyfol4
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