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Artykuły w czasopismach na temat "Whole slide images classification"
Fell, Christina, Mahnaz Mohammadi, David Morrison, Ognjen Arandjelović, Sheeba Syed, Prakash Konanahalli, Sarah Bell, Gareth Bryson, David J. Harrison i David Harris-Birtill. "Detection of malignancy in whole slide images of endometrial cancer biopsies using artificial intelligence". PLOS ONE 18, nr 3 (8.03.2023): e0282577. http://dx.doi.org/10.1371/journal.pone.0282577.
Pełny tekst źródłaGovind, Darshana, Brendon Lutnick, John E. Tomaszewski i Pinaki Sarder. "Automated erythrocyte detection and classification from whole slide images". Journal of Medical Imaging 5, nr 02 (10.04.2018): 1. http://dx.doi.org/10.1117/1.jmi.5.2.027501.
Pełny tekst źródłaNeto, Pedro C., Sara P. Oliveira, Diana Montezuma, João Fraga, Ana Monteiro, Liliana Ribeiro, Sofia Gonçalves, Isabel M. Pinto i Jaime S. Cardoso. "iMIL4PATH: A Semi-Supervised Interpretable Approach for Colorectal Whole-Slide Images". Cancers 14, nr 10 (18.05.2022): 2489. http://dx.doi.org/10.3390/cancers14102489.
Pełny tekst źródłaFranklin, Daniel L., Tara Pattilachan i Anthony Magliocco. "Abstract 5048: Imaging based EGFR mutation subtype classification using EfficientNet". Cancer Research 82, nr 12_Supplement (15.06.2022): 5048. http://dx.doi.org/10.1158/1538-7445.am2022-5048.
Pełny tekst źródłaAhmed, Shakil, Asadullah Shaikh, Hani Alshahrani, Abdullah Alghamdi, Mesfer Alrizq, Junaid Baber i Maheen Bakhtyar. "Transfer Learning Approach for Classification of Histopathology Whole Slide Images". Sensors 21, nr 16 (9.08.2021): 5361. http://dx.doi.org/10.3390/s21165361.
Pełny tekst źródłaFu, Zhibing, Qingkui Chen, Mingming Wang i Chen Huang. "Whole slide images classification model based on self-learning sampling". Biomedical Signal Processing and Control 90 (kwiecień 2024): 105826. http://dx.doi.org/10.1016/j.bspc.2023.105826.
Pełny tekst źródłaFridman, M. V., A. A. Kosareva, E. V. Snezhko, P. V. Kamlach i V. A. Kovalev. "Papillary thyroid carcinoma whole-slide images as a basis for deep learning". Informatics 20, nr 2 (29.06.2023): 28–38. http://dx.doi.org/10.37661/1816-0301-2023-20-2-28-38.
Pełny tekst źródłaJansen, Philipp, Adelaida Creosteanu, Viktor Matyas, Amrei Dilling, Ana Pina, Andrea Saggini, Tobias Schimming i in. "Deep Learning Assisted Diagnosis of Onychomycosis on Whole-Slide Images". Journal of Fungi 8, nr 9 (28.08.2022): 912. http://dx.doi.org/10.3390/jof8090912.
Pełny tekst źródłaLewis, Joshua, Xuebao Zhang, Nithya Shanmugam, Bradley Drumheller, Conrad Shebelut, Geoffrey Smith, Lee Cooper i David Jaye. "Machine Learning-Based Automated Selection of Regions for Analysis on Bone Marrow Aspirate Smears". American Journal of Clinical Pathology 156, Supplement_1 (1.10.2021): S1—S2. http://dx.doi.org/10.1093/ajcp/aqab189.001.
Pełny tekst źródłaEl-Hossiny, Ahmed S., Walid Al-Atabany, Osama Hassan, Ahmed M. Soliman i Sherif A. Sami. "Classification of Thyroid Carcinoma in Whole Slide Images Using Cascaded CNN". IEEE Access 9 (2021): 88429–38. http://dx.doi.org/10.1109/access.2021.3076158.
Pełny tekst źródłaRozprawy doktorskie na temat "Whole slide images classification"
Pirovano, Antoine. "Computer-aided diagnosis methods for cervical cancer screening on liquid-based Pap smears using convolutional neural networks : design, optimization and interpretability". Electronic Thesis or Diss., Institut polytechnique de Paris, 2021. http://www.theses.fr/2021IPPAT011.
Pełny tekst źródłaCervical cancer is the second most important cancer for women after breast cancer. In 2012, the number of cases exceeded 500,000 worldwide, among which half turned to be deadly.Until today, primary cervical cancer screening is performed by a regular visual analysis of cells, sampled by pap-smear by cytopathologists under brightfield microscopy in pathology laboratories. In France, about 5 millions of cervical screening are performed each year and about 90% lead to a negative diagnosis (i.e. no pre-cancerous changes detected). Yet, these analyses under microscope are extremely tedious and time-consuming for cytotechnicians and can require the joint opinion of several experts. This process has an impact on the capacity to tackle this huge amount of cases and to avoid false negatives that are the main cause of treatment delay. The lack of automation and traceability of screening is thus becoming more critical as the number of cyto-pathologists decreases. In that respect, the integration of digital tools in pathology laboratories is becoming a real public health stake for patients and the privileged path for the improvement of these laboratories. Since 2012, deep learning methods have revolutionized the computer vision field, in particular thanks to convolutional neural networks that have been applied successfully to a wide range of applications among which biomedical imaging. Along with it, the whole slide imaging digitization process has opened the opportunity for new efficient computer-aided diagnosis methods and tools. In this thesis, after motivating the medical needs and introducing the state-of-the-art deep learning methods for image processing and understanding, we present our contribution to the field of computer vision tackling cervical cancer screening in the context of liquid-based cytology. Our first contribution consists in proposing a simple regularization constraint for classification model training in the context of ordinal regression tasks (i.e. ordered classes). We prove the advantage of our method on cervical cells classification using Herlev dataset. Furthermore, we propose to rely on explanations from gradient-based explanations to perform weakly-supervised localization and detection of abnormality. Finally, we show how we integrate these methods as a computer-aided tool that could be used to reduce the workload of cytopathologists.The second contribution focuses on whole slide classification and the interpretability of these pipelines. We present in detail the most popular approaches for whole slide classification relying on multiple instance learning, and improve the interpretability in a context of weakly-supervised learning through tile-level feature visualizations and a novel manner of computing explanations of heat-maps. Finally, we apply these methods for cervical cancer screening by using a weakly trained “abnormality” detector for region of interest sampling that guides the training
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.
Pełny tekst źródłaAnatomic 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
Дяченко, Є. В. "Інформаційна технологія розпізнавання онкопатологій на повнослайдових гістологічних зображеннях". Master's thesis, Сумський державний університет, 2020. https://essuir.sumdu.edu.ua/handle/123456789/78594.
Pełny tekst źródłaPhillips, Adon. "Melanoma Diagnostics Using Fully Convolutional Networks on Whole Slide Images". Thesis, Université d'Ottawa / University of Ottawa, 2017. http://hdl.handle.net/10393/36929.
Pełny tekst źródłaTrahearn, Nicholas. "Registration and multi-immunohistochemical analysis of whole slide images of serial tissue sections". Thesis, University of Warwick, 2017. http://wrap.warwick.ac.uk/89986/.
Pełny tekst źródłaKhire, Sourabh Mohan. "Time-sensitive communication of digital images, with applications in telepathology". Thesis, Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/29761.
Pełny tekst źródłaCommittee Chair: Jayant, Nikil; Committee Member: Anderson, David; Committee Member: Lee, Chin-Hui. Part of the SMARTech Electronic Thesis and Dissertation Collection.
Huang, Pei-Chen, i 黃珮楨. "Real Time Automatic Lung Tumor Segmentation in Whole-slide Histopathological Images". Thesis, 2019. http://ndltd.ncl.edu.tw/handle/2h8u6r.
Pełny tekst źródłaLee, Chieh-Chi, i 李捷琦. "Computer-aided diagnosis of mycobacteria bacilli detection in digital whole slide pathological images with deep learning architecture". Thesis, 2019. http://ndltd.ncl.edu.tw/handle/738y92.
Pełny tekst źródłaMünch, Benno Jürgen Helmut. "Whole Tumor Histogramm-profiling of Diffusion-Weighted Magnetic Resonance Images reflects tumorbiological features of Primary Central Nervous System Lymphoma". 2018. https://ul.qucosa.de/id/qucosa%3A34107.
Pełny tekst źródłaCzęści książek na temat "Whole slide images classification"
Apou, Grégory, Benoît Naegel, Germain Forestier, Friedrich Feuerhake i Cédric Wemmert. "Efficient Region-based Classification for Whole Slide Images". W Communications in Computer and Information Science, 239–56. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25117-2_15.
Pełny tekst źródłaRymarczyk, Dawid, Adam Pardyl, Jarosław Kraus, Aneta Kaczyńska, Marek Skomorowski i Bartosz Zieliński. "ProtoMIL: Multiple Instance Learning with Prototypical Parts for Whole-Slide Image Classification". W Machine Learning and Knowledge Discovery in Databases, 421–36. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26387-3_26.
Pełny tekst źródłaKwok, Scotty. "Multiclass Classification of Breast Cancer in Whole-Slide Images". W Lecture Notes in Computer Science, 931–40. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93000-8_106.
Pełny tekst źródłaZhang, Jingwei, Xin Zhang, Ke Ma, Rajarsi Gupta, Joel Saltz, Maria Vakalopoulou i Dimitris Samaras. "Gigapixel Whole-Slide Images Classification Using Locally Supervised Learning". W Lecture Notes in Computer Science, 192–201. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-16434-7_19.
Pełny tekst źródłaGadermayr, Michael, Martin Strauch, Barbara Mara Klinkhammer, Sonja Djudjaj, Peter Boor i Dorit Merhof. "Domain Adaptive Classification for Compensating Variability in Histopathological Whole Slide Images". W Lecture Notes in Computer Science, 616–22. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41501-7_69.
Pełny tekst źródłaRen, Jian, Ilker Hacihaliloglu, Eric A. Singer, David J. Foran i Xin Qi. "Adversarial Domain Adaptation for Classification of Prostate Histopathology Whole-Slide Images". W Medical Image Computing and Computer Assisted Intervention – MICCAI 2018, 201–9. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00934-2_23.
Pełny tekst źródłaLi, Jiahui, Wen Chen, Xiaodi Huang, Shuang Yang, Zhiqiang Hu, Qi Duan, Dimitris N. Metaxas, Hongsheng Li i Shaoting Zhang. "Hybrid Supervision Learning for Pathology Whole Slide Image Classification". W Medical Image Computing and Computer Assisted Intervention – MICCAI 2021, 309–18. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87237-3_30.
Pełny tekst źródłaDing, Saisai, Jun Wang, Juncheng Li i Jun Shi. "Multi-scale Prototypical Transformer for Whole Slide Image Classification". W Lecture Notes in Computer Science, 602–11. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43987-2_58.
Pełny tekst źródłaShen, Yiqing, i Jing Ke. "A Deformable CRF Model for Histopathology Whole-Slide Image Classification". W Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 500–508. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59722-1_48.
Pełny tekst źródłaZheng, Yushan, Jun Li, Jun Shi, Fengying Xie i Zhiguo Jiang. "Kernel Attention Transformer (KAT) for Histopathology Whole Slide Image Classification". W Lecture Notes in Computer Science, 283–92. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-16434-7_28.
Pełny tekst źródłaStreszczenia konferencji na temat "Whole slide images classification"
Khvostikov, Alexander, Andrey Krylov, Ilya Mikhailov, Pavel Malkov i Natalya Danilova. "Tissue Type Recognition in Whole Slide Histological Images". W 31th International Conference on Computer Graphics and Vision. Keldysh Institute of Applied Mathematics, 2021. http://dx.doi.org/10.20948/graphicon-2021-3027-496-507.
Pełny tekst źródłaBug, Daniel, Julia Schuler, Friedrich Feuerhake i Dorit Merhof. "Multi-class single-label classification of histopathological whole-slide images". W 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI 2016). IEEE, 2016. http://dx.doi.org/10.1109/isbi.2016.7493527.
Pełny tekst źródłaYadav, Ankur, Ovidiu Daescu, Patrick Leavey i Erin Rudzinski. "Machine Learning for Rhabdomyosarcoma Whole Slide Images Sub-type Classification". W PETRA '23: Proceedings of the 16th International Conference on PErvasive Technologies Related to Assistive Environments. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3594806.3594865.
Pełny tekst źródłaTavolara, Thomas E., M. Khalid Khan Niazi i Metin N. Gurcan. "Background detection affects downstream classification of Camelyon16 whole slide images". W Digital and Computational Pathology, redaktorzy John E. Tomaszewski i Aaron D. Ward. SPIE, 2023. http://dx.doi.org/10.1117/12.2653882.
Pełny tekst źródłaEl-Hossiny, Ahmed S., Walid Al-Atabany, Osama Hassan, Ahmed Mostafa i Sherif A. Sami. "A robust CNN classification of whole slide thyroid carcinoma images". W 2021 9th International Japan-Africa Conference on Electronics, Communications, and Computations (JAC-ECC). IEEE, 2021. http://dx.doi.org/10.1109/jac-ecc54461.2021.9691433.
Pełny tekst źródłavan Zon, Mike, Nikolas Stathonikos, Willeke A. M. Blokx, Selim Komina, Sybren L. N. Maas, Josien P. W. Pluim, Paul J. van Diest i Mitko Veta. "Segmentation and Classification of Melanoma and Nevus in Whole Slide Images". W 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI). IEEE, 2020. http://dx.doi.org/10.1109/isbi45749.2020.9098487.
Pełny tekst źródłaGao, Zeyu, Anyu Mao, Jialun Wu, Yang Li, Chunbao Wang, Caixia Ding, Tieliang Gong i Chen Li. "Uncertainty-based Model Acceleration for Cancer Classification in Whole-Slide Images". W 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2022. http://dx.doi.org/10.1109/bibm55620.2022.9995601.
Pełny tekst źródłaZhang, Chaoyi, Yang Song, Donghao Zhang, Sidong Liu, Mei Chen i Weidong Cai. "Whole Slide Image Classification via Iterative Patch Labelling". W 2018 25th IEEE International Conference on Image Processing (ICIP). IEEE, 2018. http://dx.doi.org/10.1109/icip.2018.8451551.
Pełny tekst źródłaWetteland, Rune, Kjersti Engan, Trygve Eftestøl, Vebjørn Kvikstad i Emilius Janssen. "Multiclass Tissue Classification of Whole-Slide Histological Images using Convolutional Neural Networks". W 8th International Conference on Pattern Recognition Applications and Methods. SCITEPRESS - Science and Technology Publications, 2019. http://dx.doi.org/10.5220/0007253603200327.
Pełny tekst źródłaAlhammad, Sarah, Kun Zhao, Anthony Jennings, Peter Hobson, Daniel F. Smith, Brett Baker, Justin Staweno i Brian C. Lovell. "Efficient DNN-Based Classification of Whole Slide Gram Stain Images for Microbiology". W 2021 Digital Image Computing: Techniques and Applications (DICTA). IEEE, 2021. http://dx.doi.org/10.1109/dicta52665.2021.9647415.
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