Literatura científica selecionada sobre o tema "Histopathological tumor segmentation"
Crie uma referência precisa em APA, MLA, Chicago, Harvard, e outros estilos
Consulte a lista de atuais artigos, livros, teses, anais de congressos e outras fontes científicas relevantes para o tema "Histopathological tumor segmentation".
Ao lado de cada fonte na lista de referências, há um botão "Adicionar à bibliografia". Clique e geraremos automaticamente a citação bibliográfica do trabalho escolhido no estilo de citação de que você precisa: APA, MLA, Harvard, Chicago, Vancouver, etc.
Você também pode baixar o texto completo da publicação científica em formato .pdf e ler o resumo do trabalho online se estiver presente nos metadados.
Artigos de revistas sobre o assunto "Histopathological tumor segmentation"
Liu, Yiqing, Qiming He, Hufei Duan, Huijuan Shi, Anjia Han e Yonghong He. "Using Sparse Patch Annotation for Tumor Segmentation in Histopathological Images". Sensors 22, n.º 16 (13 de agosto de 2022): 6053. http://dx.doi.org/10.3390/s22166053.
Texto completo da fontevan der Kamp, Ananda, Thomas de Bel, Ludo van Alst, Jikke Rutgers, Marry M. van den Heuvel-Eibrink, Annelies M. C. Mavinkurve-Groothuis, Jeroen van der Laak e Ronald R. de Krijger. "Automated Deep Learning-Based Classification of Wilms Tumor Histopathology". Cancers 15, n.º 9 (8 de maio de 2023): 2656. http://dx.doi.org/10.3390/cancers15092656.
Texto completo da fonteZadeh Shirazi, Amin, Eric Fornaciari, Mark D. McDonnell, Mahdi Yaghoobi, Yesenia Cevallos, Luis Tello-Oquendo, Deysi Inca e Guillermo A. Gomez. "The Application of Deep Convolutional Neural Networks to Brain Cancer Images: A Survey". Journal of Personalized Medicine 10, n.º 4 (12 de novembro de 2020): 224. http://dx.doi.org/10.3390/jpm10040224.
Texto completo da fontePark, Youngjae, Jinhee Park e Gil-Jin Jang. "Efficient Perineural Invasion Detection of Histopathological Images Using U-Net". Electronics 11, n.º 10 (22 de maio de 2022): 1649. http://dx.doi.org/10.3390/electronics11101649.
Texto completo da fonteAltini, Nicola, Emilia Puro, Maria Giovanna Taccogna, Francescomaria Marino, Simona De Summa, Concetta Saponaro, Eliseo Mattioli, Francesco Alfredo Zito e Vitoantonio Bevilacqua. "Tumor Cellularity Assessment of Breast Histopathological Slides via Instance Segmentation and Pathomic Features Explainability". Bioengineering 10, n.º 4 (23 de março de 2023): 396. http://dx.doi.org/10.3390/bioengineering10040396.
Texto completo da fonteAlthubaity, DaifAllah D., Faisal Fahad Alotaibi, Abdalla Mohamed Ahmed Osman, Mugahed Ali Al-khadher, Yahya Hussein Ahmed Abdalla, Sadeq Abdo Alwesabi, Elsadig Eltaher Hamed Abdulrahman e Maram Abdulkhalek Alhemairy. "Automated Lung Cancer Segmentation in Tissue Micro Array Analysis Histopathological Images Using a Prototype of Computer-Assisted Diagnosis". Journal of Personalized Medicine 13, n.º 3 (23 de fevereiro de 2023): 388. http://dx.doi.org/10.3390/jpm13030388.
Texto completo da fonteMusulin, Jelena, Daniel Štifanić, Ana Zulijani, Tomislav Ćabov, Andrea Dekanić e Zlatan Car. "An Enhanced Histopathology Analysis: An AI-Based System for Multiclass Grading of Oral Squamous Cell Carcinoma and Segmenting of Epithelial and Stromal Tissue". Cancers 13, n.º 8 (8 de abril de 2021): 1784. http://dx.doi.org/10.3390/cancers13081784.
Texto completo da fonteNicolás-Sáenz, Laura, Sara Guerrero-Aspizua, Javier Pascau e Arrate Muñoz-Barrutia. "Nonlinear Image Registration and Pixel Classification Pipeline for the Study of Tumor Heterogeneity Maps". Entropy 22, n.º 9 (28 de agosto de 2020): 946. http://dx.doi.org/10.3390/e22090946.
Texto completo da fonteHuang, Zhi, Anil V. Parwani, Kun Huang e Zaibo Li. "Abstract 5436: Developing artificial intelligence algorithms to predict response to neoadjuvant chemotherapy in HER2-positive breast cancer". Cancer Research 83, n.º 7_Supplement (4 de abril de 2023): 5436. http://dx.doi.org/10.1158/1538-7445.am2023-5436.
Texto completo da fonteFagundes, Theara C., Arnoldo Mafra, Rodrigo G. Silva, Ana C. G. Castro, Luciana C. Silva, Priscilla T. Aguiar, Josiane A. Silva, Eduardo P. Junior, Alexei M. Machado e Marcelo Mamede. "Individualized threshold for tumor segmentation in 18F-FDG PET/CT imaging: The key for response evaluation of neoadjuvant chemoradiation therapy in patients with rectal cancer?" Revista da Associação Médica Brasileira 64, n.º 2 (fevereiro de 2018): 119–26. http://dx.doi.org/10.1590/1806-9282.64.02.119.
Texto completo da fonteTeses / dissertações sobre o assunto "Histopathological tumor segmentation"
Lerousseau, Marvin. "Weakly Supervised Segmentation and Context-Aware Classification in Computational Pathology". Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPASG015.
Texto completo da fonteAnatomic pathology is the medical discipline responsible for the diagnosis and characterization of diseases through the macroscopic, microscopic, molecular and immunologic inspection of tissues. Modern technologies have made possible the digitization of tissue glass slides into whole slide images, which can themselves be processed by artificial intelligence to enhance the capabilities of pathologists. This thesis presented several novel and powerful approaches that tackle pan-cancer segmentation and classification of whole slide images. Learning segmentation models for whole slide images is challenged by an annotation bottleneck which arises from (i) a shortage of pathologists, (ii) an intense cumbersomeness and boring annotation process, and (iii) major inter-annotators discrepancy. My first line of work tackled pan-cancer tumor segmentation by designing two novel state-of-the-art weakly supervised approaches that exploit slide-level annotations that are fast and easy to obtain. In particular, my second segmentation contribution was a generic and highly powerful algorithm that leverages percentage annotations on a slide basis, without needing any pixelbased annotation. Extensive large-scale experiments showed the superiority of my approaches over weakly supervised and supervised methods for pan-cancer tumor segmentation on a dataset of more than 15,000 unfiltered and extremely challenging whole slide images from snap-frozen tissues. My results indicated the robustness of my approaches to noise and systemic biases in annotations. Digital slides are difficult to classify due to their colossal sizes, which range from millions of pixels to billions of pixels, often weighing more than 500 megabytes. The straightforward use of traditional computer vision is therefore not possible, prompting the use of multiple instance learning, a machine learning paradigm consisting in assimilating a whole slide image as a set of patches uniformly sampled from it. Up to my works, the greater majority of multiple instance learning approaches considered patches as independently and identically sampled, i.e. discarded the spatial relationship of patches extracted from a whole slide image. Some approaches exploited such spatial interconnection by leveraging graph-based models, although the true domain of whole slide images is specifically the image domain which is more suited with convolutional neural networks. I designed a highly powerful and modular multiple instance learning framework that leverages the spatial relationship of patches extracted from a whole slide image by building a sparse map from the patches embeddings, which is then further processed into a whole slide image embedding by a sparse-input convolutional neural network, before being classified by a generic classifier model. My framework essentially bridges the gap between multiple instance learning, and fully convolutional classification. I performed extensive experiments on three whole slide image classification tasks, including the golden task of cancer pathologist of subtyping tumors, on a dataset of more than 20,000 whole slide images from public data. Results highlighted the superiority of my approach over all other widespread multiple instance learning methods. Furthermore, while my experiments only investigated my approach with sparse-input convolutional neural networks with two convolutional layers, the results showed that my framework works better as the number of parameters increases, suggesting that more sophisticated convolutional neural networks can easily obtain superior results
Huang, Pei-Chen, e 黃珮楨. "Real Time Automatic Lung Tumor Segmentation in Whole-slide Histopathological Images". Thesis, 2019. http://ndltd.ncl.edu.tw/handle/2h8u6r.
Texto completo da fonteCapítulos de livros sobre o assunto "Histopathological tumor segmentation"
Lerousseau, Marvin, Maria Vakalopoulou, Marion Classe, Julien Adam, Enzo Battistella, Alexandre Carré, Théo Estienne, Théophraste Henry, Eric Deutsch e Nikos Paragios. "Weakly Supervised Multiple Instance Learning Histopathological Tumor Segmentation". In Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 470–79. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59722-1_45.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Histopathological tumor segmentation"
Huang, Xiansong, Hongliang He, Pengxu Wei, Chi Zhang, Juncen Zhang e Jie Chen. "Tumor Tissue Segmentation for Histopathological Images". In MMAsia '19: ACM Multimedia Asia. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3338533.3372210.
Texto completo da fonteMusulin, Jelena, Daniel Štifanić, Ana Zulijani e Zlatan Car. "SEMANTIC SEGMENTATION OF ORAL SQUAMOUS CELL CARCINOMA ON EPITHELLIAL AND STROMAL TISSUE". In 1st INTERNATIONAL Conference on Chemo and BioInformatics. Institute for Information Technologies, University of Kragujevac, 2021. http://dx.doi.org/10.46793/iccbi21.194m.
Texto completo da fonte