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Letteratura scientifica selezionata sul tema "HISTOPATHOLOGY IMAGE"

Autore: Grafiati

Pubblicato: 26 ottobre 2023

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Articoli di riviste sul tema "HISTOPATHOLOGY IMAGE"

1

Chen, Jia-Mei, Yan Li, Jun Xu, et al. "Computer-aided prognosis on breast cancer with hematoxylin and eosin histopathology images: A review." Tumor Biology 39, no. 3 (2017): 101042831769455. http://dx.doi.org/10.1177/1010428317694550.

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With the advance of digital pathology, image analysis has begun to show its advantages in information analysis of hematoxylin and eosin histopathology images. Generally, histological features in hematoxylin and eosin images are measured to evaluate tumor grade and prognosis for breast cancer. This review summarized recent works in image analysis of hematoxylin and eosin histopathology images for breast cancer prognosis. First, prognostic factors for breast cancer based on hematoxylin and eosin histopathology images were summarized. Then, usual procedures of image analysis for breast cancer pro

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Arevalo, John, Angel Cruz-Roa, and Fabio A. González O. "Representación de imágenes de histopatología utilizada en tareas de análisis automático: estado del arte." Revista Med 22, no. 2 (2014): 79. http://dx.doi.org/10.18359/rmed.1184.

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<p>This paper presents a review of the state-of-the-art in histopathology image representation used in automatic image analysis tasks. Automatic analysis of histopathology images is important for building computer-assisted diagnosis tools, automatic image enhancing systems and virtual microscopy systems, among other applications. Histopathology images have a rich mix of visual patterns with particularities that make them difficult to analyze. The paper discusses these particularities, the acquisition process and the challenges found when doing automatic analysis. Second an overview of re

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Wang, Pin, Shanshan Lv, Yongming Li, et al. "Hybrid Deep Transfer Network and Rotational Sample Subspace Ensemble Learning for Early Cancer Detection." Journal of Medical Imaging and Health Informatics 10, no. 10 (2020): 2289–96. http://dx.doi.org/10.1166/jmihi.2020.3172.

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Accurate histopathology cell image classification plays an important role in early cancer detection and diagnosis. Currently, Convolutional Neural Network is used to assist pathologists for histopathology image classification. In the paper, a Min mice model was applied to evaluate the capability of Convolutional Neural Network features for detecting early-stage carcinogenesis. However, due to the limited histopathology images of the mice model, it may cause overfitting for the classification. Hence, hybrid deep transfer network and rotational sample subspace ensemble learning is proposed for t

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Wang, Pin, Shanshan Lv, Yongming Li, et al. "Hybrid Deep Transfer Network and Rotational Sample Subspace Ensemble Learning for Early Cancer Detection." Journal of Medical Imaging and Health Informatics 10, no. 10 (2020): 2289–96. http://dx.doi.org/10.1166/jmihi.2020.31722289.

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Accurate histopathology cell image classification plays an important role in early cancer detection and diagnosis. Currently, Convolutional Neural Network is used to assist pathologists for histopathology image classification. In the paper, a Min mice model was applied to evaluate the capability of Convolutional Neural Network features for detecting early-stage carcinogenesis. However, due to the limited histopathology images of the mice model, it may cause overfitting for the classification. Hence, hybrid deep transfer network and rotational sample subspace ensemble learning is proposed for t

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Tawfeeq, Furat Nidhal, Nada A. S. Alwan, and Basim M. Khashman. "Optimization of Digital Histopathology Image Quality." IAES International Journal of Artificial Intelligence (IJ-AI) 7, no. 2 (2018): 71. http://dx.doi.org/10.11591/ijai.v7.i2.pp71-77.

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<span lang="EN-US">One of the biomedical image problems is the appearance of the bubbles in the slide that could occur when air passes through the slide during the preparation process. These bubbles may complicate the process of analysing the histopathological images. The objective of this study is to remove the bubble noise from the histopathology images, and then predict the tissues that underlie it using the fuzzy controller in cases of remote pathological diagnosis. Fuzzy logic uses the linguistic definition to recognize the relationship between the input and the activity, rather tha

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Gupta, Rachit Kumar, Jatinder Manhas, and Mandeep Kour. "Hybrid Feature Extraction Based Ensemble Classification Model to Diagnose Oral Carcinoma Using Histopathological Images." JOURNAL OF SCIENTIFIC RESEARCH 66, no. 03 (2022): 219–26. http://dx.doi.org/10.37398/jsr.2022.660327.

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Detection and classification of cancerous tissue from histopathologic images is quite a challenging task for pathologists and computer assisted medical diagnosis systems because of the complexity of the histopathology image. For a good diagnostic system, feature extraction from the medical images plays a crucial role for better classification of images. Using inappropriate or redundant features leads to poor classification results because classification algorithm learns a lot of unimportant information from the images. We propose hybrid feature extractor using different feature extraction algo

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Rani V, Sudha, and 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, no. 2s (2022): 158–65. http://dx.doi.org/10.17762/ijritcc.v10i2s.5923.

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A cancerous tumour in a woman's breast, Histopathology detects breast cancer. Histopathological images are a hotspot for medical study since they are difficult to judge manually. In addition to helping doctors identify and treat patients, this image classification can boost patient survival. This research addresses the merits and downsides of deep learning methods for histopathology imaging of breast cancer. The study's histopathology image classification and future directions are reviewed. Automatic histopathological image analysis often uses complete supervised learning where we can feed the

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Tellez, David, Geert Litjens, Jeroen van der Laak, and Francesco Ciompi. "Neural Image Compression for Gigapixel Histopathology Image Analysis." IEEE Transactions on Pattern Analysis and Machine Intelligence 43, no. 2 (2021): 567–78. http://dx.doi.org/10.1109/tpami.2019.2936841.

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Kwak, Deawon, Jiwoo Choi, and Sungjin Lee. "Rethinking Breast Cancer Diagnosis through Deep Learning Based Image Recognition." Sensors 23, no. 4 (2023): 2307. http://dx.doi.org/10.3390/s23042307.

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This paper explored techniques for diagnosing breast cancer using deep learning based medical image recognition. X-ray (Mammography) images, ultrasound images, and histopathology images are used to improve the accuracy of the process by diagnosing breast cancer classification and by inferring their affected location. For this goal, the image recognition application strategies for the maximal diagnosis accuracy in each medical image data are investigated in terms of various image classification (VGGNet19, ResNet50, DenseNet121, EfficietNet v2), image segmentation (UNet, ResUNet++, DeepLab v3),

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Kandel, Ibrahem, Mauro Castelli, and Aleš Popovič. "Comparative Study of First Order Optimizers for Image Classification Using Convolutional Neural Networks on Histopathology Images." Journal of Imaging 6, no. 9 (2020): 92. http://dx.doi.org/10.3390/jimaging6090092.

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The classification of histopathology images requires an experienced physician with years of experience to classify the histopathology images accurately. In this study, an algorithm was developed to assist physicians in classifying histopathology images; the algorithm receives the histopathology image as an input and produces the percentage of cancer presence. The primary classifier used in this algorithm is the convolutional neural network, which is a state-of-the-art classifier used in image classification as it can classify images without relying on the manual selection of features from each

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Tesi sul tema "HISTOPATHOLOGY IMAGE"

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Chaganti, Shikha. "Image Analysis of Glioblastoma Histopathology." University of Cincinnati / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1406820611.

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DI, CATALDO SANTA. "Image Processing Techniques for Histopathology." Doctoral thesis, Politecnico di Torino, 2011. http://hdl.handle.net/11583/2586367.

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In the last few years biologists and pathologists are relying more and more on image analysis, and immunohistochemistry (IHC) is nowadays one of the most popular imaging techniques to analyze the presence and activity of target antigens in the tissues, with important applications in the diagnosis and assessment of tumors as well as for several research purposes. However, immunohistochemistry has been traditionally affected by lack of reproducibility due to technological variabilities as well as to the inherent subjectivity of the visual observation, thus the analysis has been limited to qualit

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Sertel, Olcay. "Image Analysis for Computer-aided Histopathology." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1276791696.

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Haddad, 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.

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The primary method for the diagnostic interpretation of histopathologic sections is visual analysis. However, in a small, but significant percentage of cases, histopathologists do not come to a consensus. Therefore, due to the importance of early and accurate detection of tissue changes indicative of pathology, quantitative image analysis techniques have been applied to this problem. The accurate segmentation of image structures such as cells and glands in histopathological sections, as with all "natural scenes", proves challenging. This has led to the development of an additional segmentation

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Traore, Lamine. "Semantic modeling of an histopathology image exploration and analysis tool." Thesis, Paris 6, 2017. http://www.theses.fr/2017PA066621/document.

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La formalisation des données cliniques est réalisée et adoptée dans plusieurs domaines de la santé comme la prévention des erreurs médicales, la standardisation, les guides de bonnes pratiques et de recommandations. Cependant, la communauté n'arrive pas encore à tirer pleinement profit de la valeur de ces données. Le problème majeur reste la difficulté à intégrer ces données et des services sémantiques associés au profit de la qualité de soins. Objectif L'objectif méthodologique de ce travail consiste à formaliser, traiter et intégrer les connaissances d'histopathologie et d'imagerie basées su

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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.

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Renal clear cell carcinoma affects the kidneys by abnormal cell division which spreads to other organs through the bloodstream and lymphatic system. The number of renal cancer cases grows whilst rapid and accurate diagnoses are required for early intervention. Biopsies are critical for cancer diagnosis. Pathologists look beyond manual evaluation to include computer-based analysis to develop accurate cancer diagnostics. Pathologists render diagnostic reports to assist with treatment whilst expert analysis is time consuming and restricts early diagnosis. The process of manual expert pathology re

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Kårsnäs, 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.

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In 2012, more than 1.6 million new cases of breast cancer were diagnosed and about half a million women died of breast cancer. The incidence has increased in the developing world. The mortality, however, has decreased. This is thought to partly be the result of advances in diagnosis and treatment. Studying tissue samples from biopsies through a microscope is an important part of diagnosing breast cancer. Recent techniques include camera-equipped microscopes and whole slide scanning systems that allow for digital high-throughput scanning of tissue samples. The introduction of digital pathology

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Fanchon, Louise. "Autoradiographie quantitative d'échantillons prélevés par biopsie guidée par TEP/TDM : méthode et applications cliniques." Thesis, Brest, 2016. http://www.theses.fr/2016BRES0018.

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Au cours des dix dernières années, l’utilisation de l’imagerie par tomographie par émission de positrons (TEP) s’est rapidement développée en oncologie. Certaines tumeurs non visibles en imagerie anatomique conventionnelle sont détectables en mesurant l'activité métabolique dans le corps humain par TEP. L’imagerie TEP est utilisée pour guider la délivrance de traitements locaux tels que par rayonnement ionisants ou ablation thermique. Pour la délivrance de ces traitements, segmenter la zone tumorale avec précision est primordial. Cependant, la faible résolution spatiale des images TEP rend la

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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.

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Brain and nervous system tumors were responsible for around 250,000 deaths in 2020 worldwide. Correctly identifying different tumors is very important, because treatment options largely depend on the diagnosis. This is an expert task, but recently machine learning, and especially deep learning models have shown huge potential in tumor classification problems, and can provide fast and reliable support for pathologists in the decision making process. This thesis investigates classification of two brain tumors, glioblastoma multiforme and lower grade glioma in high-resolution H&E-stained hist

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Azar, 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.

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Automated tissue image analysis aims to develop algorithms for a variety of histological applications. This has important implications in the diagnostic grading of cancer such as in breast and prostate tissue, as well as in the quantification of prognostic and predictive biomarkers that may help assess the risk of recurrence and the responsiveness of tumors to endocrine therapy. In this thesis, we use pattern recognition and image analysis techniques to solve several problems relating to histopathology and immunohistochemistry applications. In particular, we present a new method for the detect

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Libri sul tema "HISTOPATHOLOGY IMAGE"

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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., and European Society of Pathology, eds. Quantitative image analysis in cancer cytology and histology. Elsevier Science, 1986.

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Y, 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), and European Society of Pathology, eds. Quantitative image analysis in cancer cytology and histology: Based on a symposium. Elsevier, 1986.

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Chevanne, Marta, and Riccardo Caldini. Immagini di Istopatologia. Firenze University Press, 2007. http://dx.doi.org/10.36253/978-88-5518-023-8.

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This collection of images of Histopathology is the fruit of the authors' thirty years' experience in the performance of practical exercises in General Pathology. It is aimed at students attending lessons of General Pathology on the Degree Courses in Medical Surgery and Biological Sciences. It does not aspire either to be complete from the point of view of the various organic pathologies, or to replace direct and personal observation of the histological preparations through the microscope, but is rather intended as an aid to students preparing for the exam. It does not include the rudiments of

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Tibor, Tot, and Dean Peter B, eds. Breast cancer: The art and science of early detection with mammography : perception, interpretation, histopathologic correlation. Thieme, 2005.

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Capitoli di libri sul tema "HISTOPATHOLOGY IMAGE"

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Mohanty, Manoranjan, and Wei Tsang Ooi. "Histopathology Image Streaming." In Advances in Multimedia Information Processing – PCM 2012. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34778-8_50.

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Chhoker, Ayush, Kunlika Saxena, Vipin Rai, and Vishwadeepak Singh Baghela. "Histopathology Osteosarcoma Image Classification." In Proceedings of International Conference on Recent Trends in Computing. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8825-7_15.

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Ortega-Gil, Ana, Arrate Muñoz-Barrutia, Laura Fernandez-Terron, and Juan José Vaquero. "Tuberculosis Histopathology on X Ray CT." In Image Analysis for Moving Organ, Breast, and Thoracic Images. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00946-5_18.

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Bueno, Gloria, Oscar Déniz, Jesús Salido, M. Milagro Fernández, Noelia Vállez, and Marcial García-Rojo. "Colour Model Analysis for Histopathology Image Processing." In Color Medical Image Analysis. Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-5389-1_9.

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Shi, Xiaoshuang, Fuyong Xing, Yuanpu Xie, Hai Su, and Lin Yang. "Cell Encoding for Histopathology Image Classification." In Lecture Notes in Computer Science. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66185-8_4.

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Wei, Jerry, Arief Suriawinata, Bing Ren, et al. "A Petri Dish for Histopathology Image Analysis." In Artificial Intelligence in Medicine. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77211-6_2.

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Li, Chen, Dan Xue, Fanjie Kong, et al. "Cervical Histopathology Image Classification Using Ensembled Transfer Learning." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23762-2_3.

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Ahmed, Hamza Kamel, Baraa Tantawi, Malak Magdy, and Gehad Ismail Sayed. "Quantum Optimized AlexNet for Histopathology Breast Image Diagnosis." In Proceedings of the 9th International Conference on Advanced Intelligent Systems and Informatics 2023. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43247-7_31.

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Tan, Jing Wei, and Won-Ki Jeong. "Histopathology Image Classification Using Deep Manifold Contrastive Learning." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43987-2_66.

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Roy, Bijoyeta, and Mousumi Gupta. "Macroscopic Reconstruction for Histopathology Images: A Survey." In Computer Vision and Machine Intelligence in Medical Image Analysis. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8798-2_11.

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Atti di convegni sul tema "HISTOPATHOLOGY IMAGE"

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Mannam, Varun, Yide Zhang, Yinhao Zhu, and Scott Howard. "Instant Image Denoising Plugin for ImageJ using Convolutional Neural Networks." In Microscopy Histopathology and Analytics. OSA, 2020. http://dx.doi.org/10.1364/microscopy.2020.mw2a.3.

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Tsai, Sheng-Ting, Chin-Cheng Chan, Homer H. Chen, Jeng-Wei Tjiu, and Sheng-Lung Huang. "Segmentation based OCT Image to H&E-like Image Conversion." In Microscopy Histopathology and Analytics. OSA, 2020. http://dx.doi.org/10.1364/microscopy.2020.mm3a.5.

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Sugie, Kenji, Kiyotaka Sasagawa, Mark Christian Guinto, Makito Haruta, Takashi Tokuda, and Jun Ohta. "Image refocusing of miniature CMOS image sensor with angle-selective pixels." In Microscopy Histopathology and Analytics. OSA, 2020. http://dx.doi.org/10.1364/microscopy.2020.mth3a.5.

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Rueden, Curtis T., and Kevin Eliceiri. "The ImageJ Ecosystem: An Open and Extensible Platform for Biomedical Image Analysis." In Microscopy Histopathology and Analytics. OSA, 2018. http://dx.doi.org/10.1364/microscopy.2018.mth2a.3.

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Li, Xinyang, Zhifeng Zhao, Guoxun Zhang, Hui Qiao, Haoqian Wang, and Qinghai Dai. "High-fidelity fluorescence image restoration using deep unsupervised learning." In Microscopy Histopathology and Analytics. OSA, 2020. http://dx.doi.org/10.1364/microscopy.2020.mw2a.2.

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Wang, Hongda, Yair Rivenson, Yiyin Jin, et al. "Deep learning-based super-resolution and image transformation into structured illumination microscopy." In Microscopy Histopathology and Analytics. OSA, 2020. http://dx.doi.org/10.1364/microscopy.2020.mm3a.4.

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Sikaroudi, Milad, Benyamin Ghojogh, Fakhri Karray, Mark Crowley, and H. R. Tizhoosh. "Magnification Generalization For Histopathology Image Embedding." In 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI). IEEE, 2021. http://dx.doi.org/10.1109/isbi48211.2021.9433978.

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Hou, Le, Kunal Singh, Dimitris Samaras, et al. "Automatic histopathology image analysis with CNNs." In 2016 New York Scientific Data Summit (NYSDS). IEEE, 2016. http://dx.doi.org/10.1109/nysds.2016.7747812.

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"Customized EfficientNet for Histopathology Image Representation." In 2022 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2022. http://dx.doi.org/10.1109/ssci51031.2022.10022191.

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T, Soumya. "Detection and Differentiation of blood cancer cells using Edge Detection method." In 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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Abstract (sommario):

Medical imaging is an essential data source that has been leveraged worldwide in health- care systems. In pathology, histopathology images are used for cancer diagnosis, whereas these images are very complex and their analyses by pathologists require large amounts of time and effort. On the other hand, although convolutional neural networks (CNNs) have produced near-human results in image processing tasks, their processing time is becoming longer and they need higher computational power. In this paper, we implement a quantized ResNet model on two histopathology image datasets to optimize the i

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