Academic literature on the topic 'Lesion detection'
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Journal articles on the topic "Lesion detection"
Zrzavy, Tobias, Alice Wielandner, Lukas Haider, Sophie Bartsch, Fritz Leutmezer, Thomas Berger, Karl Heinz Nenning, Alexander Rauscher, Paulus Rommer, and Gregor Kasprian. "FLAIR2 post-processing: improving MS lesion detection in standard MS imaging protocols." Journal of Neurology 269, no. 1 (October 8, 2021): 461–67. http://dx.doi.org/10.1007/s00415-021-10833-x.
Full textCorreia, J., A. Ponte, L. Proença, A. Rodrigues, R. Pinho, S. Leite, C. Fernandes, et al. "P206 Comparison of dye-spraying chromoendoscopy and virtual chromoendoscopy for colonic dysplasia detection in longstanding Inflammatory Bowel Disease." Journal of Crohn's and Colitis 16, Supplement_1 (January 1, 2022): i265. http://dx.doi.org/10.1093/ecco-jcc/jjab232.333.
Full textWattjes, Mike P., Martijn T. Wijburg, Anke Vennegoor, Birgit I. Witte, Stefan D. Roosendaal, Esther Sanchez, Yaou Liu, et al. "Diagnostic performance of brain MRI in pharmacovigilance of natalizumab-treated MS patients." Multiple Sclerosis Journal 22, no. 9 (July 20, 2016): 1174–83. http://dx.doi.org/10.1177/1352458515615225.
Full textMetser, Ur, Orit Golan, Charles D. Levine, and Einat Even-Sapir. "Tumor Lesion Detection." Journal of Computer Assisted Tomography 29, no. 4 (July 2005): 554–59. http://dx.doi.org/10.1097/01.rct.0000164671.96143.c2.
Full textBhardwaj, Charu, Shruti Jain, and Meenakshi Sood. "Automated Diagnostic Hybrid Lesion Detection System for Diabetic Retinopathy Abnormalities." International Journal of Sensors, Wireless Communications and Control 10, no. 4 (December 18, 2020): 494–507. http://dx.doi.org/10.2174/2210327909666191126092411.
Full textKuklyte, Jogile, Jenny Fitzgerald, Sophie Nelissen, Haolin Wei, Aoife Whelan, Adam Power, Ajaz Ahmad, et al. "Evaluation of the Use of Single- and Multi-Magnification Convolutional Neural Networks for the Determination and Quantitation of Lesions in Nonclinical Pathology Studies." Toxicologic Pathology 49, no. 4 (February 23, 2021): 815–42. http://dx.doi.org/10.1177/0192623320986423.
Full textMatsui, Hiroaki, Shunsuke Kamba, Hideka Horiuchi, Sho Takahashi, Masako Nishikawa, Akihiro Fukuda, Aya Tonouchi, et al. "Detection Accuracy and Latency of Colorectal Lesions with Computer-Aided Detection System Based on Low-Bias Evaluation." Diagnostics 11, no. 10 (October 17, 2021): 1922. http://dx.doi.org/10.3390/diagnostics11101922.
Full textRezzo, R., G. Scopinaro, M. Gambaro, P. Michetti, and G. Anfossi. "Radioguided Occult Colonic Lesion Identification (Rocli) during Open and Laparoscopic Surgery." Tumori Journal 88, no. 3 (May 2002): S19—S22. http://dx.doi.org/10.1177/030089160208800328.
Full textSen Saxena, Vivek, Prashant Johri, and Avneesh Kumar. "AI-Enabled Support System for Melanoma Detection and Classification." International Journal of Reliable and Quality E-Healthcare 10, no. 4 (October 2021): 58–75. http://dx.doi.org/10.4018/ijrqeh.2021100104.
Full textO'Brien, W. J., L. Vazquez, and W. M. Johnston. "The Detection of Incipient Caries with Tracer Dyes." Journal of Dental Research 68, no. 2 (February 1989): 157–58. http://dx.doi.org/10.1177/00220345890680021101.
Full textDissertations / Theses on the topic "Lesion detection"
Eltayef, Khalid Ahmad A. "Segmentation and lesion detection in dermoscopic images." Thesis, Brunel University, 2017. http://bura.brunel.ac.uk/handle/2438/16211.
Full textRolland, Jannick Paule Yvette. "Factors influencing lesion detection in medical imaging." Diss., The University of Arizona, 1990. http://hdl.handle.net/10150/185096.
Full textNagane, Radhika. "Detection of flash in dermoscopy skin lesion images." Diss., Rolla, Mo. : University of Missouri-Rolla, 2007. http://scholarsmine.umr.edu/thesis/pdf/Nagane_09007dcc803ec3f9.pdf.
Full textVita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed December 7, 2007) Includes bibliographical references (p. 89-90).
Pons, Rodríguez Gerard. "Computer-aided lesion detection and segmentation on breast ultrasound." Doctoral thesis, Universitat de Girona, 2014. http://hdl.handle.net/10803/129453.
Full textAquesta tesi es centra en la detecció, segmentació i classificació de lesions en imatges d'ecografia. La contribució d'aquesta tesi és el desenvolupament d'una nova eina de Diagnòstic Assistit per Ordinador (DAO) capaç de detectar, segmentar i classificar automàticament lesions en imatges d'ecografia de mama. Inicialment, s'ha proposat l'adaptació del mètode genèric de detecció d'objectes Deformable Part Models (DPM) per detectar lesions en imatges d'ecografia. Aquest mètode utilitza tècniques d'aprenentatge automàtic per generar un model basat en l'Histograma de Gradients Orientats. Aquest mètode també és utilitzat per detectar lesions malignes directament, simplificant així l'estratègia tradicional. A continuació, s'han realitzat diferents propostes d'inicialització en un mètode de segmentació basat en Markov Random Field (MRF)-Maximum A Posteriori (MAP) per tal de reduir la interacció amb l'usuari. Per avaluar aquesta proposta, s'ha realitzat un estudi sobre la influència del tipus de lesió en els resultats aconseguits. Finalment, s'ha proposat la inclusió d'elastografia en aquesta estratègia de segmentació. Els mètodes proposats per a cada etapa de l'eina DAO han estat avaluats fent servir bases de dades diferents, comparant els resultats obtinguts amb els resultats dels mètodes més importants de l'estat de l'art
Gonzalez, Ana Guadalupe Salazar. "Structure analysis and lesion detection from retinal fundus images." Thesis, Brunel University, 2011. http://bura.brunel.ac.uk/handle/2438/6456.
Full textGomez, Bulla Juliana. "Detection, diagnosis and management of the early carious lesion." Thesis, University of Manchester, 2013. https://www.research.manchester.ac.uk/portal/en/theses/detection-diagnosis-and-management-of-the-early-carious-lesion(f7ae030d-fe41-4e3d-802a-a3cd8c0e978d).html.
Full textAgarwal, Richa. "Computer aided detection for breast lesion in ultrasound and mammography." Doctoral thesis, Universitat de Girona, 2019. http://hdl.handle.net/10803/670295.
Full textEn el camp de les imatges de càncer de mama, els sistemes tradicionals de detecció assistida per ordinador (de l’anglès CAD) es van dissenyar utilitzant recursos informàtics limitats i pel·lícules de mamografia escanejades (del angles SFM) de qualitat d’imatge deficient, fet que va resultar en aplicacions poc robustes. Actualment, amb els avanços de les tecnologies, és possible realitzar imatges mèdiques en 3D i adquirir mamografies digitals (de l’anglès FFDM) d’alta qualitat. L’ultrasò automàtic de la mama (de l’anglès ABUS) ha estat proposat per adquirir imatges 3D de la mama amb escassa dependència del operador. Quan s’utilitza ABUS, la segmentació i seguiment de les lesions en el temps s ́on tasques complicades ja que la naturalesa 3D de les imatges fa que l’anàlisi sigui difícil i feixuc per els radiòlegs. Un dels objectius d’aquesta tesi és desenvolupar un marc per la segmentació semi-automàtica de lesions mamàries en volums ABUS. El volum de lesió 3D, en combinació amb l’anàlisi de la textura i el contorn, podria proporcionar informació valuosa per realitzar el diagnòstic radiològic. Tot i que els volums de ABUS són de gran interès, la mamografia de raigs X continua essent la modalitat d’imatge estàndard utilitzada per la detecció precoç del càncer de mama, degut principalment a la seva ràpida adquisició i rendibilitat. A més, amb l’arribada dels mètodes d’aprenentatge profund basats en xarxes neuronals convolucionals (de l’anglès CNN), els sistemes CAD moderns poden aprendre automàticament quines característiques de la imatge són més rellevants per realitzar un diagnòstic, fet que augmenta la utilitat d’aquests sistemes. Una de les limitacions de les CNN és que requereixen de grans conjunts de dades per entrenar, els quals són molt limitats en el camp de la imatge mèdica. En aquesta tesi, el tema de la poca disponibilitat d’imatges mediques s’aborda mitjançant dues estratègies: (i) utilitzant regions de la imatge com a entrada en comptes de les imatges de mida original, i (ii) mitjançant tècniques d’aprenentatge per transferència, en el que el coneixement après per a una determinada tasca es transfereix a una altra tasca relacionada (també conegut com a adaptació de domini). En primer lloc, la CNN entrenada en un conjunt de dades molt gran d’imatges naturals és adaptada per classificar regions de la imatge en tumor i no tumor de SFM i, en segon lloc, la CNN entrenada és adaptada per detectar tumors en FFDM. També s’ha investigat l’aprenentatge per transferència entre imatges naturals i FFDM. S’han utilitzat dos conjunts de dades públiques (CBIS-DDSM i INbreast) per aquest propòsit. En la fase final de la investigació, es proposa un marc de detecció automàtica de tumors utilitzant la mamografia original com entrada (en lloc de regions de la imatge) i que proporciona la localització de la lesió dins d’aquesta mamografia com a sortida. Per aquest propòsit s’utilitza una altra base de dades (OMI-DB). Els resultats obtinguts com a part d’aquesta tesi mostren millors rendiments en comparació amb l’estat de l’art, el que indica que els mètodes i marcs proposats tenen el potencial de ser implementats dins de sistemes CAD avançats, que poden ser utilitzats per radiòlegs en el cribratge del càncer de mama
Yap, Moi Hoon. "Enhanced algorithms for lesion detection and recognition in ultrasound breast images." Thesis, Loughborough University, 2008. https://dspace.lboro.ac.uk/2134/35018.
Full textAlaverdyan, Zaruhi. "Unsupervised representation learning for anomaly detection on neuroimaging. Application to epilepsy lesion detection on brain MRI." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSEI005/document.
Full textThis work represents one attempt to develop a computer aided diagnosis system for epilepsy lesion detection based on neuroimaging data, in particular T1-weighted and FLAIR MR sequences. Given the complexity of the task and the lack of a representative voxel-level labeled data set, the adopted approach, first introduced in Azami et al., 2016, consists in casting the lesion detection task as a per-voxel outlier detection problem. The system is based on training a one-class SVM model for each voxel in the brain on a set of healthy controls, so as to model the normality of the voxel. The main focus of this work is to design representation learning mechanisms, capturing the most discriminant information from multimodality imaging. Manual features, designed to mimic the characteristics of certain epilepsy lesions, such as focal cortical dysplasia (FCD), on neuroimaging data, are tailored to individual pathologies and cannot discriminate a large range of epilepsy lesions. Such features reflect the known characteristics of lesion appearance; however, they might not be the most optimal ones for the task at hand. Our first contribution consists in proposing various unsupervised neural architectures as potential feature extracting mechanisms and, eventually, introducing a novel configuration of siamese networks, to be plugged into the outlier detection context. The proposed system, evaluated on a set of T1-weighted MRIs of epilepsy patients, showed a promising performance but a room for improvement as well. To this end, we considered extending the CAD system so as to accommodate multimodality data which offers complementary information on the problem at hand. Our second contribution, therefore, consists in proposing strategies to combine representations of different imaging modalities into a single framework for anomaly detection. The extended system showed a significant improvement on the task of epilepsy lesion detection on T1-weighted and FLAIR MR images. Our last contribution focuses on the integration of PET data into the system. Given the small number of available PET images, we make an attempt to synthesize PET data from the corresponding MRI acquisitions. Eventually we show an improved performance of the system when trained on the mixture of synthesized and real images
Slimani, Amel. "Photonic approach for the study of dental hard tissues and carious lesion detection." Thesis, Montpellier, 2017. http://www.theses.fr/2017MONTT125.
Full textPhotonic properties of dental hard tissues allowed us to proceed to in vitro analysis of enamel and dentin on a molecular level. Confocal Raman microscopy has been used to produce a mapping of collagen cross-link and crystallinity of human dentin–enamel junction (DEJ) with a spatial resolution not achieved up to now. The method is a non-invasive, label-free and a high spatial resolution imaging technique. This chemical analysis of DEJ led us to redefine a wider width of this transition zone and advance our understanding of dental histology. A study on the intrinsic fluorescence changes of sound and carious tissues using conventional fluorescence microscopy suggests the involvement of protoporphyrin IX and pentosidine in the fluorescence red-shift observed in carious tissues. Multiphoton microscopy allowed to detect nonlinear optical signal changes during caries process using second harmonic generation (SHG) and two-photon excitation fluorescence (2PEF). Our studies led us to propose the ratio SHG/2PEF as valuable parameter to monitor caries lesion. Collectively, advances described in this thesis show the potential of photonic properties of enamel and dentin using Raman and multiphoton microcopies for molecular investigations on sound as much as on carious tissues. It opens new perspective in dental research and clinical applications
Books on the topic "Lesion detection"
McNab, Claire. Fatal reunion: A Detective Inspector Carol Ashtonmystery. Tallahassee, FL: Naiad Press, 1989.
Find full textKing, Laurie R. The Art of Detection. New York: Random House Publishing Group, 2006.
Find full textKing, Laurie R. The art of detection. New York: Bantam Dell, 2006.
Find full textThe art of detection. Thorndike, Me: Center Point Pub., 2006.
Find full textPennypacker, Leslye Carol. Improved detection of human breast lesions following experimental training II: A medical student replication. [New Haven: s.n.], 1985.
Find full textThe Lesson of Her Death. New York: Random House Publishing Group, 2009.
Find full textDeaver, Jeffery. The lesson of her death. New York: Bantam Books, 1994.
Find full textThe lesson of her death. London: Headline, 1993.
Find full textDeaver, Jeffery. The lesson of her death. New York: Doubleday, 1993.
Find full textThe lesson of her death. London: Coronet Books, 1994.
Find full textBook chapters on the topic "Lesion detection"
Neuhaus, K. W., R. Ellwood, A. Lussi, and N. B. Pitts. "Traditional Lesion Detection Aids." In Monographs in Oral Science, 42–51. Basel: KARGER, 2009. http://dx.doi.org/10.1159/000224211.
Full textNeuhaus, K. W., C. Longbottom, R. Ellwood, and A. Lussi. "Novel Lesion Detection Aids." In Monographs in Oral Science, 52–62. Basel: KARGER, 2009. http://dx.doi.org/10.1159/000224212.
Full textIşın, Ali, and Tazeen Sharif. "Deep Learning for Lung Lesion Detection." In 13th International Conference on Theory and Application of Fuzzy Systems and Soft Computing — ICAFS-2018, 799–806. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04164-9_105.
Full textLi, Han, Hu Han, and S. Kevin Zhou. "Bounding Maps for Universal Lesion Detection." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 417–28. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59719-1_41.
Full textManjaramkar, Arati, and Manesh Kokare. "Automated Red Lesion Detection: An Overview." In Advances in Intelligent Systems and Computing, 177–88. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1483-8_16.
Full textLongbottom, Christopher, and Bruce Vernon. "Bioluminescence Technology to Aid Lesion Activity Assessment." In Detection and Assessment of Dental Caries, 217–24. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16967-1_22.
Full textLi, He, Yutaro Iwamoto, Xianhua Han, Lanfen Lin, Hongjie Hu, and Yen-Wei Chen. "An Accurate Unsupervised Liver Lesion Detection Method Using Pseudo-lesions." In Lecture Notes in Computer Science, 214–23. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-16452-1_21.
Full textMarrone, Stefano, Gabriele Piantadosi, Roberta Fusco, Antonella Petrillo, Mario Sansone, and Carlo Sansone. "Automatic Lesion Detection in Breast DCE-MRI." In Image Analysis and Processing – ICIAP 2013, 359–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41184-7_37.
Full textSong, Yang, Weidong Cai, Heng Huang, Xiaogang Wang, Stefan Eberl, Michael Fulham, and Dagan Feng. "Similarity Guided Feature Labeling for Lesion Detection." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013, 284–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40811-3_36.
Full textChen, Xiaoran, Nick Pawlowski, Ben Glocker, and Ender Konukoglu. "Unsupervised Lesion Detection with Locally Gaussian Approximation." In Machine Learning in Medical Imaging, 355–63. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32692-0_41.
Full textConference papers on the topic "Lesion detection"
Bai, Bing, Yongzhao Du, Ping Li, and Yuchun Lv. "Cervical Lesion Detection Net." In 2019 IEEE 13th International Conference on Anti-counterfeiting, Security, and Identification (ASID). IEEE, 2019. http://dx.doi.org/10.1109/icasid.2019.8925284.
Full textDallali, Adel, Amel Slimen, Salim El Khediri, and Youssra Khemili. "Detection of lesion in mammograms." In 2018 International Conference on Advanced Systems and Electric Technologies (IC_ASET). IEEE, 2018. http://dx.doi.org/10.1109/aset.2018.8379902.
Full textBurgess, Arthur E., Francine L. Jacobson, and Philip F. Judy. "Lesion detection in digital mammograms." In Medical Imaging 2001, edited by Larry E. Antonuk and Martin J. Yaffe. SPIE, 2001. http://dx.doi.org/10.1117/12.430878.
Full textReiazi, Reza, Reza Paydar, Ali Abbasian Ardakani, and Maryam Etedadialiabadi. "Mammography Lesion Detection Using Faster R-CNN Detector." In 7th International Conference on Natural Language Processing. Academy & Industry Research Collaboration Center (AIRCC), 2018. http://dx.doi.org/10.5121/csit.2018.80212.
Full textChen, Kecheng, Kun Long, Yazhou Ren, Jiayu Sun, and Xiaorong Pu. "Lesion-Inspired Denoising Network: Connecting Medical Image Denoising and Lesion Detection." In MM '21: ACM Multimedia Conference. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3474085.3475480.
Full textLuan, Bo, Yunxu Sun, Cheng Tong, Yuanxian Liu, and Hongshun Liu. "R-FCN Based Laryngeal Lesion Detection." In 2019 12th International Symposium on Computational Intelligence and Design (ISCID). IEEE, 2019. http://dx.doi.org/10.1109/iscid.2019.10112.
Full textHatanaka, Yuji, Atsuki Mizukami, Chisako Muramatsu, Takeshi Hara, and Hiroshi Fujita. "Automated lesion detection in retinal images." In the 4th International Symposium. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2093698.2093789.
Full textDjaouti, S. Mohammed, A. Koudache, A. Boudaïeb, Arno P. J. M. Siebes, Michael R. Berthold, Robert C. Glen, and Ad J. Feelders. "Polarimetric images segmentation for lesion detection." In COMPLIFE 2007: The Third International Symposium on Computational Life Science. AIP, 2007. http://dx.doi.org/10.1063/1.2793393.
Full textSreena, S., and A. Lijiya. "Skin Lesion Analysis Towards Melanoma Detection." In 2019 2nd International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT). IEEE, 2019. http://dx.doi.org/10.1109/icicict46008.2019.8993219.
Full textRehman, Hafeez Ur, Syed Adnan Shah, Wakeel Ahmad, Syed Muhammad Anwar, and Nudrat Nida. "Deep retinanet for melanoma lesion detection." In 2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2). IEEE, 2022. http://dx.doi.org/10.1109/icodt255437.2022.9787454.
Full textReports on the topic "Lesion detection"
Udupa, Jayaram K. A Novel Fuzzy Topological Approach to the Detection of Mammographic Lesion and Quantification of Parenchymal Density. Fort Belvoir, VA: Defense Technical Information Center, August 1998. http://dx.doi.org/10.21236/adb241926.
Full textLelievre, Sophie. Channeling Nanoparticles for Detection and Targeted Treatment of Breast Cancerous Lesions. Fort Belvoir, VA: Defense Technical Information Center, October 2011. http://dx.doi.org/10.21236/ada555798.
Full textLam, Stephen, John Yee, Wan L. Lam, Don Wilson, and Calum MacAulay. Blood-Based Biomarkers for Lung Cancer Early Detection and Evaluation of CT-Based Lesions. Fort Belvoir, VA: Defense Technical Information Center, December 2013. http://dx.doi.org/10.21236/ada614404.
Full textUdupa, Jayaram K. A Novel Fuzzy Topological Approach to the Detection of Mammographic Lesions and Qualifications of Parenchymal Density. Fort Belvoir, VA: Defense Technical Information Center, August 1999. http://dx.doi.org/10.21236/ada382475.
Full textZhang, Chunxi, Fangfang Xie, Runchang Li, Ningxin Cui, and Jiayuan Sun. Robotic-assisted bronchoscopy for the diagnosis of peripheral pulmonary lesions: A systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, September 2022. http://dx.doi.org/10.37766/inplasy2022.9.0115.
Full textEdwards, Darrin C., Charles E. Metz, and Maryellen L. Giger. Investigation of Three-Group Classifiers to Fully Automate Detection and Classification of Breast Lesions in an Intelligent CAD Mammography Workstation. Fort Belvoir, VA: Defense Technical Information Center, May 2007. http://dx.doi.org/10.21236/ada472082.
Full textThomas Austin, Evan, Paul Kang, Chinedu Mmeje, Joseph Mashni, Mark Brenner, Phillip Koo, and John C Chang. Validation of PI-RADS v2 Scores at Various Non-University Radiology Practices. Science Repository, December 2021. http://dx.doi.org/10.31487/j.aco.2021.02.02.
Full textLeonard, Talayna, Robert Lemme, Cati Kral, Briana Santiago, Chris Elberts, Stephanie Dewald, Patrick McGonagill, et al. High-Percentage of Early Resectable Pancreatic Ductal Adenocarcinoma is Unidentified on Abdominal CT Obtained for Unrelated Diagnosis. Science Repository, December 2021. http://dx.doi.org/10.31487/j.aco.2021.02.03.
Full textRenaud, Alexander, Michael Forte, Nicholas Spore, Brittany Bruder, Katherine Brodie, Jessamin Straub, and Jeffrey Ruby. Evaluation of Unmanned Aircraft Systems for flood risk management : results of terrain and structure assessments. Engineer Research and Development Center (U.S.), August 2022. http://dx.doi.org/10.21079/11681/45000.
Full textSmit, Amelia, Kate Dunlop, Nehal Singh, Diona Damian, Kylie Vuong, and Anne Cust. Primary prevention of skin cancer in primary care settings. The Sax Institute, August 2022. http://dx.doi.org/10.57022/qpsm1481.
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