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Artykuły w czasopismach na temat "HISTOPATHOLOGY IMAGE"
Chen, Jia-Mei, Yan Li, Jun Xu, Lei Gong, Lin-Wei Wang, Wen-Lou Liu i Juan Liu. "Computer-aided prognosis on breast cancer with hematoxylin and eosin histopathology images: A review". Tumor Biology 39, nr 3 (marzec 2017): 101042831769455. http://dx.doi.org/10.1177/1010428317694550.
Pełny tekst źródłaArevalo, John, Angel Cruz-Roa i Fabio A. González O. "Representación de imágenes de histopatología utilizada en tareas de análisis automático: estado del arte". Revista Med 22, nr 2 (1.12.2014): 79. http://dx.doi.org/10.18359/rmed.1184.
Pełny tekst źródłaWang, Pin, Shanshan Lv, Yongming Li, Qi Song, Linyu Li, Jiaxin Wang i Hehua Zhang. "Hybrid Deep Transfer Network and Rotational Sample Subspace Ensemble Learning for Early Cancer Detection". Journal of Medical Imaging and Health Informatics 10, nr 10 (1.10.2020): 2289–96. http://dx.doi.org/10.1166/jmihi.2020.3172.
Pełny tekst źródłaWang, Pin, Shanshan Lv, Yongming Li, Qi Song, Linyu Li, Jiaxin Wang i Hehua Zhang. "Hybrid Deep Transfer Network and Rotational Sample Subspace Ensemble Learning for Early Cancer Detection". Journal of Medical Imaging and Health Informatics 10, nr 10 (1.10.2020): 2289–96. http://dx.doi.org/10.1166/jmihi.2020.31722289.
Pełny tekst źródłaTawfeeq, Furat Nidhal, Nada A. S. Alwan i Basim M. Khashman. "Optimization of Digital Histopathology Image Quality". IAES International Journal of Artificial Intelligence (IJ-AI) 7, nr 2 (20.04.2018): 71. http://dx.doi.org/10.11591/ijai.v7.i2.pp71-77.
Pełny tekst źródłaGupta, Rachit Kumar, Jatinder Manhas i Mandeep Kour. "Hybrid Feature Extraction Based Ensemble Classification Model to Diagnose Oral Carcinoma Using Histopathological Images". JOURNAL OF SCIENTIFIC RESEARCH 66, nr 03 (2022): 219–26. http://dx.doi.org/10.37398/jsr.2022.660327.
Pełny tekst źródłaRani V, Sudha, i M. Jogendra Kumar. "Histopathological Image Classification Methods and Techniques in Deep Learning Field". International Journal on Recent and Innovation Trends in Computing and Communication 10, nr 2s (31.12.2022): 158–65. http://dx.doi.org/10.17762/ijritcc.v10i2s.5923.
Pełny tekst źródłaTellez, David, Geert Litjens, Jeroen van der Laak i Francesco Ciompi. "Neural Image Compression for Gigapixel Histopathology Image Analysis". IEEE Transactions on Pattern Analysis and Machine Intelligence 43, nr 2 (1.02.2021): 567–78. http://dx.doi.org/10.1109/tpami.2019.2936841.
Pełny tekst źródłaKwak, Deawon, Jiwoo Choi i Sungjin Lee. "Rethinking Breast Cancer Diagnosis through Deep Learning Based Image Recognition". Sensors 23, nr 4 (19.02.2023): 2307. http://dx.doi.org/10.3390/s23042307.
Pełny tekst źródłaKandel, Ibrahem, Mauro Castelli i Aleš Popovič. "Comparative Study of First Order Optimizers for Image Classification Using Convolutional Neural Networks on Histopathology Images". Journal of Imaging 6, nr 9 (8.09.2020): 92. http://dx.doi.org/10.3390/jimaging6090092.
Pełny tekst źródłaRozprawy doktorskie na temat "HISTOPATHOLOGY IMAGE"
Chaganti, Shikha. "Image Analysis of Glioblastoma Histopathology". University of Cincinnati / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1406820611.
Pełny tekst źródłaDI, CATALDO SANTA. "Image Processing Techniques for Histopathology". Doctoral thesis, Politecnico di Torino, 2011. http://hdl.handle.net/11583/2586367.
Pełny tekst źródłaSertel, Olcay. "Image Analysis for Computer-aided Histopathology". The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1276791696.
Pełny tekst źródłaHaddad, Jane Wurster 1965. "Evaluation of diagnostic clues in histopathology through image processing techniques". Thesis, The University of Arizona, 1990. http://hdl.handle.net/10150/277296.
Pełny tekst źródłaTraore, Lamine. "Semantic modeling of an histopathology image exploration and analysis tool". Thesis, Paris 6, 2017. http://www.theses.fr/2017PA066621/document.
Pełny tekst źródłaSemantic modelling of a histopathology image exploration and analysis tool. Recently, anatomic pathology (AP) has seen the introduction of several tools such as high-resolution histopathological slide scanners, efficient software viewers for large-scale histopathological images and virtual slide technologies. These initiatives created the conditions for a broader adoption of computer-aided diagnosis based on whole slide images (WSI) with the hope of a possible contribution to decreasing inter-observer variability. Beside this, automatic image analysis algorithms represent a very promising solution to support pathologist’s laborious tasks during the diagnosis process. Similarly, in order to reduce inter-observer variability between AP reports of malignant tumours, the College of American Pathologists edited 67 organ-specific Cancer Checklists and associated Protocols (CAP-CC&P). Each checklist includes a set of AP observations that are relevant in the context of a given organ-specific cancer and have to be reported by the pathologist. The associated protocol includes interpretation guidelines for most of the required observations. All these changes and initiatives bring up a number of scientific challenges such as the sustainable management of the available semantic resources associated to the diagnostic interpretation of AP images by both humans and computers. In this context, reference vocabularies and formalization of the associated knowledge are especially needed to annotate histopathology images with labels complying with semantic standards. In this research work, we present our contribution in this direction. We propose a sustainable way to bridge the content, features, performance and usability gaps between histopathology and WSI analysis
Hossain, Md Shamim. "An automated deep learning based approach for nuclei segmentation of renal digital histopathology image analysis". Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2022. https://ro.ecu.edu.au/theses/2611.
Pełny tekst źródłaKårsnäs, Andreas. "Image Analysis Methods and Tools for Digital Histopathology Applications Relevant to Breast Cancer Diagnosis". Doctoral thesis, Uppsala universitet, Avdelningen för visuell information och interaktion, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-219306.
Pełny tekst źródłaFanchon, Louise. "Autoradiographie quantitative d'échantillons prélevés par biopsie guidée par TEP/TDM : méthode et applications cliniques". Thesis, Brest, 2016. http://www.theses.fr/2016BRES0018.
Pełny tekst źródłaDuring the last decade, positron emission tomography (PET) has been finding broader application in oncology. Some tumors that are non-visible in standard anatomic imaging like computerized tomography (CT) or ultrasounds, can be detected by measuring in 3D the metabolic activity of the body, using PET imaging. PET images can also be used to deliver localized therapy like radiation therapy or ablation. In order to deliver localized therapy, the tumor border has to be delineated with very high accuracy. However, the poor spatial resolution of PET images makes the segmentation challenging. Studies have shown that manual segmentation introduces a large inter- and intra- variability, and is very time consuming. For these reasons, many automatic segmentation algorithms have been developed. However, few datasets with histopathological information are available to test and validate these algorithms since it is experimentally difficult to produce them. The aim of the method developed was to evaluate PET segmentation algorithms against the underlying histopathology. This method consists in acquiring quantitative autoradiography of biopsy specimen extracted under PET/CT guidance. The autoradiography allows imaging the radiotracer distribution in the biopsy specimen with a very high spatial accuracy. Histopathological sections of the specimen can then obtained and observed under the microscope. The autoradiography and the micrograph of the histological sections can then be registered with the PET image, by aligning them first with the biopsy needle seen on the CT image and then transferring them onto the PET image. The next step was to use this dataset to test two PET automatic segmentation algorithms: the Fuzzy Locally Adaptive Bayesian (FLAB) developed at the Laboratory of Medical Information Processing (LaTIM) in Brest, France, as well as a fix threshold segmentation method. However, the reliability of the dataset produced depends on the accuracy of the registration of the PET, autoradiography and micrograph images. The main source of uncertainty in the registration of these images comes from the registration between the CT and the PET. In order to evaluate the accuracy of the registration, a method was developed. The results obtained with this method showed that the registration error ranges from 1.1 to 10.9mm. Based on those results, the dataset obtained from 4 patients was judged satisfying to test the segmentation algorithms. The comparison of the contours obtained with FLAB and with the fixed threshold method shows that at the point of biopsy, the FLAB contour is closer than that to the histopathology contour. However, the two segmentation methods give similar contours, because the lesions were homogeneous
Hrabovszki, Dávid. "Classification of brain tumors in weakly annotated histopathology images with deep learning". Thesis, Linköpings universitet, Statistik och maskininlärning, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177271.
Pełny tekst źródłaAzar, Jimmy. "Automated Tissue Image Analysis Using Pattern Recognition". Doctoral thesis, Uppsala universitet, Bildanalys och människa-datorinteraktion, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-231039.
Pełny tekst źródłaKsiążki na temat "HISTOPATHOLOGY IMAGE"
Y, Mary J., Rigaut J. P, Unité de recherches biomathématiques et biostatistiques., Institut national de la santé et de la recherche médicale., Association pour la recherche sur le cancer. i European Society of Pathology, red. Quantitative image analysis in cancer cytology and histology. Amsterdam: Elsevier Science, 1986.
Znajdź pełny tekst źródłaY, Mary J., Rigaut J. P, Institut national de la santé et de la recherche médicale (France). Unité de recherches biomathématiques et biostatistiques., Association pour le développment de la recherche sur le cancer (France) i European Society of Pathology, red. Quantitative image analysis in cancer cytology and histology: Based on a symposium. Amsterdam: Elsevier, 1986.
Znajdź pełny tekst źródłaChevanne, Marta, i Riccardo Caldini. Immagini di Istopatologia. Florence: Firenze University Press, 2007. http://dx.doi.org/10.36253/978-88-5518-023-8.
Pełny tekst źródłaTibor, Tot, i Dean Peter B, red. Breast cancer: The art and science of early detection with mammography : perception, interpretation, histopathologic correlation. Stuttgart: Thieme, 2005.
Znajdź pełny tekst źródłaCzęści książek na temat "HISTOPATHOLOGY IMAGE"
Mohanty, Manoranjan, i Wei Tsang Ooi. "Histopathology Image Streaming". W Advances in Multimedia Information Processing – PCM 2012, 534–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34778-8_50.
Pełny tekst źródłaChhoker, Ayush, Kunlika Saxena, Vipin Rai i Vishwadeepak Singh Baghela. "Histopathology Osteosarcoma Image Classification". W Proceedings of International Conference on Recent Trends in Computing, 163–74. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8825-7_15.
Pełny tekst źródłaOrtega-Gil, Ana, Arrate Muñoz-Barrutia, Laura Fernandez-Terron i Juan José Vaquero. "Tuberculosis Histopathology on X Ray CT". W Image Analysis for Moving Organ, Breast, and Thoracic Images, 169–79. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00946-5_18.
Pełny tekst źródłaBueno, Gloria, Oscar Déniz, Jesús Salido, M. Milagro Fernández, Noelia Vállez i Marcial García-Rojo. "Colour Model Analysis for Histopathology Image Processing". W Color Medical Image Analysis, 165–80. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-5389-1_9.
Pełny tekst źródłaShi, Xiaoshuang, Fuyong Xing, Yuanpu Xie, Hai Su i Lin Yang. "Cell Encoding for Histopathology Image Classification". W Lecture Notes in Computer Science, 30–38. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66185-8_4.
Pełny tekst źródłaWei, Jerry, Arief Suriawinata, Bing Ren, Xiaoying Liu, Mikhail Lisovsky, Louis Vaickus, Charles Brown i in. "A Petri Dish for Histopathology Image Analysis". W Artificial Intelligence in Medicine, 11–24. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77211-6_2.
Pełny tekst źródłaLi, Chen, Dan Xue, Fanjie Kong, Zhijie Hu, Hao Chen, Yudong Yao, Hongzan Sun i in. "Cervical Histopathology Image Classification Using Ensembled Transfer Learning". W Advances in Intelligent Systems and Computing, 26–37. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23762-2_3.
Pełny tekst źródłaAhmed, Hamza Kamel, Baraa Tantawi, Malak Magdy i Gehad Ismail Sayed. "Quantum Optimized AlexNet for Histopathology Breast Image Diagnosis". W Proceedings of the 9th International Conference on Advanced Intelligent Systems and Informatics 2023, 348–57. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43247-7_31.
Pełny tekst źródłaTan, Jing Wei, i Won-Ki Jeong. "Histopathology Image Classification Using Deep Manifold Contrastive Learning". W Lecture Notes in Computer Science, 683–92. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43987-2_66.
Pełny tekst źródłaRoy, Bijoyeta, i Mousumi Gupta. "Macroscopic Reconstruction for Histopathology Images: A Survey". W Computer Vision and Machine Intelligence in Medical Image Analysis, 101–12. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8798-2_11.
Pełny tekst źródłaStreszczenia konferencji na temat "HISTOPATHOLOGY IMAGE"
Mannam, Varun, Yide Zhang, Yinhao Zhu i Scott Howard. "Instant Image Denoising Plugin for ImageJ using Convolutional Neural Networks". W Microscopy Histopathology and Analytics. Washington, D.C.: OSA, 2020. http://dx.doi.org/10.1364/microscopy.2020.mw2a.3.
Pełny tekst źródłaTsai, Sheng-Ting, Chin-Cheng Chan, Homer H. Chen, Jeng-Wei Tjiu i Sheng-Lung Huang. "Segmentation based OCT Image to H&E-like Image Conversion". W Microscopy Histopathology and Analytics. Washington, D.C.: OSA, 2020. http://dx.doi.org/10.1364/microscopy.2020.mm3a.5.
Pełny tekst źródłaSugie, Kenji, Kiyotaka Sasagawa, Mark Christian Guinto, Makito Haruta, Takashi Tokuda i Jun Ohta. "Image refocusing of miniature CMOS image sensor with angle-selective pixels". W Microscopy Histopathology and Analytics. Washington, D.C.: OSA, 2020. http://dx.doi.org/10.1364/microscopy.2020.mth3a.5.
Pełny tekst źródłaRueden, Curtis T., i Kevin Eliceiri. "The ImageJ Ecosystem: An Open and Extensible Platform for Biomedical Image Analysis". W Microscopy Histopathology and Analytics. Washington, D.C.: OSA, 2018. http://dx.doi.org/10.1364/microscopy.2018.mth2a.3.
Pełny tekst źródłaLi, Xinyang, Zhifeng Zhao, Guoxun Zhang, Hui Qiao, Haoqian Wang i Qinghai Dai. "High-fidelity fluorescence image restoration using deep unsupervised learning". W Microscopy Histopathology and Analytics. Washington, D.C.: OSA, 2020. http://dx.doi.org/10.1364/microscopy.2020.mw2a.2.
Pełny tekst źródłaWang, Hongda, Yair Rivenson, Yiyin Jin, Zhensong Wei, Ronald Gao, Harun Günaydın, Laurent A. Bentolila, Comert Kural i Aydogan Ozcan. "Deep learning-based super-resolution and image transformation into structured illumination microscopy". W Microscopy Histopathology and Analytics. Washington, D.C.: OSA, 2020. http://dx.doi.org/10.1364/microscopy.2020.mm3a.4.
Pełny tekst źródłaSikaroudi, Milad, Benyamin Ghojogh, Fakhri Karray, Mark Crowley i H. R. Tizhoosh. "Magnification Generalization For Histopathology Image Embedding". W 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI). IEEE, 2021. http://dx.doi.org/10.1109/isbi48211.2021.9433978.
Pełny tekst źródłaHou, Le, Kunal Singh, Dimitris Samaras, Tahsin M. Kurc, Yi Gao, Roberta J. Seidman i Joel H. Saltz. "Automatic histopathology image analysis with CNNs". W 2016 New York Scientific Data Summit (NYSDS). IEEE, 2016. http://dx.doi.org/10.1109/nysds.2016.7747812.
Pełny tekst źródła"Customized EfficientNet for Histopathology Image Representation". W 2022 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2022. http://dx.doi.org/10.1109/ssci51031.2022.10022191.
Pełny tekst źródłaT, Soumya. "Detection and Differentiation of blood cancer cells using Edge Detection method". W The International Conference on scientific innovations in Science, Technology, and Management. International Journal of Advanced Trends in Engineering and Management, 2023. http://dx.doi.org/10.59544/zbua6077/ngcesi23p138.
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