Relevant bibliographies by topics / Whole-slide image

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Whole-slide image'

Author: Grafiati
Published: 5 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Whole-slide image"

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Elmore, Joann G., Hannah Shucard, Annie C. Lee, et al. "Pathology Trainees’ Experience and Attitudes on Use of Digital Whole Slide Images." Academic Pathology 7 (January 1, 2020): 237428952095192. http://dx.doi.org/10.1177/2374289520951922.

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Digital whole slide images are Food and Drug Administration approved for clinical diagnostic use in pathology; however, integration is nascent. Trainees from 9 pathology training programs completed an online survey to ascertain attitudes toward and experiences with whole slide images for pathological interpretations. Respondents (n = 76) reported attending 63 unique medical schools (45 United States, 18 international). While 63% reported medical school exposure to whole slide images, most reported ≤ 5 hours. Those who began training more recently were more likely to report at least some exposu
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Rjabceva, S. N., V. A. Kovalev, V. D. Malyshev, et al. "Development of neoplastic region selection algorithm based on breast cancer whole slide image." Doklady BGUIR 18, no. 8 (2020): 21–28. http://dx.doi.org/10.35596/1729-7648-2020-18-8-21-28.

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Analysis of breast cancer whole-slide image is an extremely labor-intensive process. Histological whole slide images have the following features: a high degree of tissue diversity both in one image and between different images, hierarchy, a large amount of graphic information and different artifacts. In this work, pre-processing of breast cancer whole-slide tissue image was carried out, which included normalization of the color distribution and the image area selection. We reduced the operating time of the other algorithms and excluded areas of breast cancer whole-slide tissue with a backgroun
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Wei, Bih-Rong, Charles H. Halsey, Shelley B. Hoover, et al. "Agreement in Histological Assessment of Mitotic Activity Between Microscopy and Digital Whole Slide Images Informs Conversion for Clinical Diagnosis." Academic Pathology 6 (January 1, 2019): 237428951985984. http://dx.doi.org/10.1177/2374289519859841.

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Validating digital pathology as substitute for conventional microscopy in diagnosis remains a priority to assure effectiveness. Intermodality concordance studies typically focus on achieving the same diagnosis by digital display of whole slide images and conventional microscopy. Assessment of discrete histological features in whole slide images, such as mitotic figures, has not been thoroughly evaluated in diagnostic practice. To further gauge the interchangeability of conventional microscopy with digital display for primary diagnosis, 12 pathologists examined 113 canine naturally occurring mu
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Tak, Yoon-Oh, Anjin Park, Janghoon Choi, Jonghyun Eom, Hyuk-Sang Kwon, and Joo Beom Eom. "Simple Shading Correction Method for Brightfield Whole Slide Imaging." Sensors 20, no. 11 (2020): 3084. http://dx.doi.org/10.3390/s20113084.

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Whole slide imaging (WSI) refers to the process of creating a high-resolution digital image of a whole slide. Since digital images are typically produced by stitching image sequences acquired from different fields of view, the visual quality of the images can be degraded owing to shading distortion, which produces black plaid patterns on the images. A shading correction method for brightfield WSI is presented, which is simple but robust not only against typical image artifacts caused by specks of dust and bubbles, but also against fixed-pattern noise, or spatial variations in pixel values unde
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Yang, Xinda, Ranze Zhang, Yuan Yang, Yu Zhang, and Kai Chen. "PathEX: Make good choice for whole slide image extraction." PLOS ONE 19, no. 8 (2024): e0304702. http://dx.doi.org/10.1371/journal.pone.0304702.

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Background The tile-based approach has been widely used for slide-level predictions in whole slide image (WSI) analysis. However, the irregular shapes and variable dimensions of tumor regions pose challenges for the process. To address this issue, we proposed PathEX, a framework that integrates intersection over tile (IoT) and background over tile (BoT) algorithms to extract tile images around boundaries of annotated regions while excluding the blank tile images within these regions. Methods We developed PathEX, which incorporated IoT and BoT into tile extraction, for training a classification
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K.P. Shivamurthy and Dr. Raju.A. S. "Optimal Whole Slide Image Segmentation Using Generalized Normal Distribution Optimization." International Research Journal on Advanced Engineering Hub (IRJAEH) 2, no. 05 (2024): 1341–47. http://dx.doi.org/10.47392/irjaeh.2024.0185.

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Whole slide image (WSI) segmentation is a crucial task aiding tumour and cancerous cell diagnosis. Generalized Normal Distribution Optimization (GNDO) algorithm is adopted for whole slide image segmentation based on thresholding in this paper. GNDO algorithm utilizes the generalized normal distribution's properties to determine the ideal thresholds for image segmentation. Through various metrics, the efficacy of GNDO in comparison to traditional Otsu thresholding methods is demonstrated. As demonstrated by the results, it can offer reliable and flexible solutions for different histopathology i
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Cucoranu, Ioan, Anil V. Parwani, Liron Pantanowitz, Malini Srinivasan, and Jon Duboy. "Impact of Whole Slide Image Integrity on Image Analysis." American Journal of Clinical Pathology 140, suppl 1 (2013): A154. http://dx.doi.org/10.1093/ajcp/140.suppl1.154.

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van Diest, Paul J., André Huisman, Jaap van Ekris, et al. "Pathology Image Exchange: The Dutch Digital Pathology Platform for Exchange of Whole-Slide Images for Efficient Teleconsultation, Telerevision, and Virtual Expert Panels." JCO Clinical Cancer Informatics, no. 3 (December 2019): 1–7. http://dx.doi.org/10.1200/cci.18.00146.

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Among the many uses of digital pathology, remote consultation, remote revision, and virtual slide panels may be the most important ones. This requires basic slide scanner infrastructure in participating laboratories to produce whole-slide images. More importantly, a software platform is needed for exchange of these images and functionality to support the processes around discussing and reporting on these images without breaching patient privacy. This poses high demands on the setup of such a platform, given the inherent complexity of the handling of digital pathology images. In this article, w
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Webster, J. D., and R. W. Dunstan. "Whole-Slide Imaging and Automated Image Analysis." Veterinary Pathology 51, no. 1 (2013): 211–23. http://dx.doi.org/10.1177/0300985813503570.

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Morrison, Annie O., and Jerad M. Gardner. "Microscopic Image Photography Techniques of the Past, Present, and Future." Archives of Pathology & Laboratory Medicine 139, no. 12 (2015): 1558–64. http://dx.doi.org/10.5858/arpa.2014-0315-ra.

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Context The field of pathology is driven by microscopic images. Educational activities for trainees and practicing pathologists alike are conducted through exposure to images of a variety of pathologic entities in textbooks, publications, online tutorials, national and international conferences, and interdepartmental conferences. During the past century and a half, photographic technology has progressed from primitive and bulky, glass-lantern projector slides to static and/or whole slide digital-image formats that can now be transferred around the world in a matter of moments via the Internet.
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Dissertations / Theses on the topic "Whole-slide image"

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Zaidi, Fatima. "Deep learning-based scale-invariant cancer detection from whole slide image." Thesis, Zaidi, Fatima (2021) Deep learning-based scale-invariant cancer detection from whole slide image. Masters by Research thesis, Murdoch University, 2021. https://researchrepository.murdoch.edu.au/id/eprint/63326/.

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Convential cancer diagnosis methods from whole slide images (WSI) train a deep Convolutional Neural Network (CNN) to make patch level predictions, and then aggregate the image-level predictions to classify a tumour as either benign or malignant. To classify a patch, the CNN extracts features through convolutional layers and then process the feature maps using fully connected layers. The size of the filters used in the convolutional layers defines the receptive field of the network. Small filters are computationally efficient but do not capture a large context. On the other hand, large filters
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Rydell, Christopher. "Deep Learning for Whole Slide Image Cytology : A Human-in-the-Loop Approach." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-450356.

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With cancer being one of the leading causes of death globally, and with oral cancers being among the most common types of cancer, it is of interest to conduct large-scale oral cancer screening among the general population. Deep Learning can be used to make this possible despite the medical expertise required for early detection of oral cancers. A bottleneck of Deep Learning is the large amount of data required to train a good model. This project investigates two topics: certainty calibration, which aims to make a machine learning model produce more reliable predictions, and Active Learning, wh
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Khire, 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.

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Thesis (M. S.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2010.<br>Committee Chair: Jayant, Nikil; Committee Member: Anderson, David; Committee Member: Lee, Chin-Hui. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Phillips, 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.

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Semantic segmentation as an approach to recognizing and localizing objects within an image is a major research area in computer vision. Now that convolutional neural networks are being increasingly used for such tasks, there have been many improve- ments in grand challenge results, and many new research opportunities in previously untennable areas. Using fully convolutional networks, we have developed a semantic segmentation pipeline for the identification of melanocytic tumor regions, epidermis, and dermis lay- ers in whole slide microscopy images of cutaneous melanoma or cutaneous metastati
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Trahearn, 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/.

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The identification and classification of tissue abnormalities for the purpose of disease diagnosis have been greatly served by the discipline of histopathology, and Immunohistochemistry (IHC) in particular. The advent of digital slide scanners and computerised slide viewing software have opened the door for introducing automated algorithms into what has traditionally been a predominantly manual discipline. Multi-IHC analysis is one potential area of interest for automation, which will be discussed in detail in this work. Analysis occurs on serial sections of tissue, which must be realigned bef
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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.

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L’anatomopathologie est la discipline médicale responsable du diagnostic et de la caractérisation des maladies par inspection macroscopique, microscopique, moléculaire et immunologique des tissus. Les technologies modernes permettent de numériser des lames tissulaire en images numériques qui peuvent être traitées par l’intelligence artificielle pour démultiplier les capacités des pathologistes. Cette thèse a présenté plusieurs approches nouvelles et puissantes qui s’attaquent à la segmentation et à la classification pan-cancer des images de lames numériques. L’apprentissage de modèles de segme
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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.

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Le cancer du col de l’utérus est le deuxième cancer le plus important pour les femmes après le cancer du sein. En 2012, le nombre de cas recensés dépasse 500,000 à travers le monde, dont la moitié se sont révélés mortels. Jusqu'à maintenant, le dépistage primaire du cancer du col de l’utérus est réalisé par l’inspection visuelle de cellules, prélevées par frottis vaginal, par des cytopathologistes utilisant la microscopie en fond clair dans des laboratoires de pathologie. En France, environ 5 millions de dépistage sont réalisés chaque année et environ 90% mènent à un diagnostic négatifs (i.e.
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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.

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Виконано аналіз метаданих повнослайдових гістологічних зображень та отримано результати їх впливу на швидкодію і точність класифікаційного алгоритму. Розроблено програмний модуль онкодіагностування з використанням методу опорних векторів SVM та виконана його оптимізація, в результаті якої алгоритм здатен встановлювати вірний діагноз з точністю 95%. Розроблений модуль створено за допомогою мови програмування Python та імпортовано до WSI-системи QuPath.
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Venâncio, Rui Miguel Morgado. "Micrometastasis detection guidance by whole-slide image texture analysis in colorectal lymph nodes correlated with QUS parameters." Master's thesis, 2016. http://hdl.handle.net/10316/32150.

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Dissertação de Mestrado em Engenharia Biomédica apresentada à Faculdade de Ciências e Tecnologia da Universidade de Coimbra.<br>O cancro ´e uma doen¸ca que afeta milh˜oes por todo o mundo e uma identifica¸c˜ao correta de gˆanglios linf´aticos pr´oximos do tumor prim´ario, que contenham regi˜oes metast´aticas ´e de extrema importˆancia para um correto gerenciamento dos pacientes. A avalia¸c˜ao histopatol´ogica ´e o ´unico m´etodo aceite para fazer essa identifica¸c˜ao. Novas t´ecnicas emergentes como os ultrassons quantitativos podem ajudar nessa identifica¸c˜ao, detetando regi˜oes metast´
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Rosenbloom, Raymond. "Multiplex immunohistochemical analysis of granulomatous inflammation in lung tissue sections using a mouse model of M. avium infection." Thesis, 2020. https://hdl.handle.net/2144/41719.

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INTRODUCTION: Investigating mechanisms of how intracellular bacterial pathogens such as Mycobacterium. avium (M. avium) evade the host immune response and replicate within macrophages is crucial to devising rational targets for host-directed therapies (HDT) against these associated diseases. This studied utilized the congenic mouse strain B6.Sst1S, which contains the super-susceptibility to tuberculosis (TB) allele. Among murine models of TB, this strain uniquely replicates human disease because mice develop granulomas with central caseous necrosis. Utilizing a susceptible model for M. avium i
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Book chapters on the topic "Whole-slide image"

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Dangott, Bryan. "Whole Slide Image Analysis." In Whole Slide Imaging. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-83332-9_12.

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Jafarinia, Hossein, Alireza Alipanah, Saeed Razavi, Nahal Mirzaie, and Mohammad Hossein Rohban. "Snuffy: Efficient Whole Slide Image Classifier." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-73024-5_15.

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Qiu, Jingna, Marc Aubreville, Frauke Wilm, et al. "Leveraging Image Captions for Selective Whole Slide Image Annotation." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-72390-2_20.

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Akram, Saad Ullah, Talha Qaiser, Simon Graham, Juho Kannala, Janne Heikkilä, and Nasir Rajpoot. "Leveraging Unlabeled Whole-Slide-Images for Mitosis Detection." In Computational Pathology and Ophthalmic Medical Image Analysis. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00949-6_9.

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Qiu, Jingna, Marc Aubreville, Frauke Wilm, et al. "Abstract: Leveraging Image Captions for Selective Whole Slide Image Annotation." In Informatik aktuell. Springer Fachmedien Wiesbaden, 2025. https://doi.org/10.1007/978-3-658-47422-5_59.

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Bug, Daniel, Felix Bartsch, Nadine Sarah Schaadt, et al. "Scalable HEVC for Histological Whole-Slide Image Compression." In Informatik aktuell. Springer Fachmedien Wiesbaden, 2020. http://dx.doi.org/10.1007/978-3-658-29267-6_71.

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Vo, Hoang, Jun Kong, Dejun Teng, et al. "Cloud-Based Whole Slide Image Analysis Using MapReduce." In Data Management and Analytics for Medicine and Healthcare. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57741-8_5.

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Zhang, Ling, Boxiang Yun, Xingran Xie, Qingli Li, Xinxing Li, and Yan Wang. "Prompting Whole Slide Image Based Genetic Biomarker Prediction." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-72083-3_38.

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Menon, Ashish, Piyush Singh, P. K. Vinod, and C. V. Jawahar. "Interactive Learning for Assisting Whole Slide Image Annotation." In Lecture Notes in Computer Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-02444-3_38.

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Fan, Lei, Arcot Sowmya, Erik Meijering, and Yang Song. "Learning Visual Features by Colorization for Slide-Consistent Survival Prediction from Whole Slide Images." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87237-3_57.

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Conference papers on the topic "Whole-slide image"

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Takano, Nana, Satoshi Nishiguchi, and Masahiro Toyoura. "Whole Slide Image Annotation Support for Estimating Lesion Proportions." In 2024 International Conference on Cyberworlds (CW). IEEE, 2024. https://doi.org/10.1109/cw64301.2024.00071.

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Gupta, Ravi Kant, Dadi Dharani, Shambhavi Shanker, and Amit Sethi. "Efficient Whole Slide Image Classification Through Fisher Vector Representation." In 2024 IEEE 24th International Conference on Bioinformatics and Bioengineering (BIBE). IEEE, 2024. https://doi.org/10.1109/bibe63649.2024.10820480.

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Wu, Zhifeng, Xiaohui Li, Luning Wang, Shendi Wang, Yufei Cui, and Jiahai Wang. "ProtoTree-MIL: Interpretable Multiple Instance Learning for Whole Slide Image Classification." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10650015.

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Abdullah, Tao Huang, Ickjai Lee, and Euijoon Ahn. "High-Resolution Histopathology Whole Slide Image Generation Using Wavelet Diffusion Model." In 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI). IEEE, 2025. https://doi.org/10.1109/isbi60581.2025.10981258.

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Du, Xianglong, JiaQi Guo, Zehang Xing, et al. "Hard example mining in Multi-Instance Learning for Whole-Slide Image Classification." In 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2024. https://doi.org/10.1109/embc53108.2024.10782609.

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Zheng, Tingting, Kui Jiang, and Hongxun Yao. "Dynamic Policy-Driven Adaptive Multi-Instance Learning for Whole Slide Image Classification." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2024. http://dx.doi.org/10.1109/cvpr52733.2024.00767.

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Wan, Jiayu, Yingguang Hao, Xiaorui Ma, and Hongyu Wang. "Multi-Scale Graph-Based Cross-Attention Transformer for Whole Slide Image Classification." In 2024 18th International Conference on Control, Automation, Robotics and Vision (ICARCV). IEEE, 2024. https://doi.org/10.1109/icarcv63323.2024.10918883.

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Ahuja, Anu, Arthur Morley-Bunker, and Ramakrishnan Mukundan. "Deep Learning Classification of Microsatellite Status in Colorectal Cancer Whole Slide Images." In 2024 39th International Conference on Image and Vision Computing New Zealand (IVCNZ). IEEE, 2024. https://doi.org/10.1109/ivcnz64857.2024.10794480.

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Hao, Yatong, Mingjie Wei, Shuang Zhao, Xiaodong Duan, and Yiqun Yao. "Multi-Scale Context-Aware Survival Prediction Network Based on Whole Slide Images." In 2024 IEEE 8th International Conference on Vision, Image and Signal Processing (ICVISP). IEEE, 2024. https://doi.org/10.1109/icvisp64524.2024.10959355.

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Zheng, Lin, Wen An, Yonggui Huang, Qingli Li, and Qing Zhang. "YOLO Based Intelligent Recognition of Planktonic Algae in Whole Slide Microscopic Images." In 2024 17th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, 2024. https://doi.org/10.1109/cisp-bmei64163.2024.10906131.

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