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Journal articles on the topic 'Whole-slide image'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

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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Zarella, Mark D., Matthew R. Quaschnick;, David E. Breen, and Fernando U. Garcia. "Estimation of Fine-Scale Histologic Features at Low Magnification." Archives of Pathology & Laboratory Medicine 142, no. 11 (2018): 1394–402. http://dx.doi.org/10.5858/arpa.2017-0380-oa.

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Context.— Whole-slide imaging has ushered in a new era of technology that has fostered the use of computational image analysis for diagnostic support and has begun to transfer the act of analyzing a slide to computer monitors. Due to the overwhelming amount of detail available in whole-slide images, analytic procedures—whether computational or visual—often operate at magnifications lower than the magnification at which the image was acquired. As a result, a corresponding reduction in image resolution occurs. It is unclear how much information is lost when magnification is reduced, and whether

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Roszkowiak, Lukasz, and Carlos Lopez. "PATMA: parser of archival tissue microarray." PeerJ 4 (December 1, 2016): e2741. http://dx.doi.org/10.7717/peerj.2741.

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The tissue microarrays are commonly used in modern pathology for cancer tissue evaluation, as it is a very potent technique. Tissue microarray slides are often scanned to perform computer-aided histopathological analysis of the tissue cores. For processing the image, splitting the whole virtual slide into images of individual cores is required. The only way to distinguish cores corresponding to specimens in the tissue microarray is through their arrangement. Unfortunately, distinguishing the correct order of cores is not a trivial task as they are not labelled directly on the slide. The main a

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Lotz, J., J. Olesch, B. Muller, et al. "Patch-Based Nonlinear Image Registration for Gigapixel Whole Slide Images." IEEE Transactions on Biomedical Engineering 63, no. 9 (2016): 1812–19. http://dx.doi.org/10.1109/tbme.2015.2503122.

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Jenkinson, Eleanor, and Ognjen Arandjelović. "Whole Slide Image Understanding in Pathology: What Is the Salient Scale of Analysis?" BioMedInformatics 4, no. 1 (2024): 489–518. http://dx.doi.org/10.3390/biomedinformatics4010028.

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Background: In recent years, there has been increasing research in the applications of Artificial Intelligence in the medical industry. Digital pathology has seen great success in introducing the use of technology in the digitisation and analysis of pathology slides to ease the burden of work on pathologists. Digitised pathology slides, otherwise known as whole slide images, can be analysed by pathologists with the same methods used to analyse traditional glass slides. Methods: The digitisation of pathology slides has also led to the possibility of using these whole slide images to train machi

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Dimitriou, Neofytos, Ognjen Arandjelović, and David J. Harrison. "Magnifying Networks for Histopathological Images with Billions of Pixels." Diagnostics 14, no. 5 (2024): 524. http://dx.doi.org/10.3390/diagnostics14050524.

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Amongst the other benefits conferred by the shift from traditional to digital pathology is the potential to use machine learning for diagnosis, prognosis, and personalization. A major challenge in the realization of this potential emerges from the extremely large size of digitized images, which are often in excess of 100,000 × 100,000 pixels. In this paper, we tackle this challenge head-on by diverging from the existing approaches in the literature—which rely on the splitting of the original images into small patches—and introducing magnifying networks (MagNets). By using an attention mechanis

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Collazo, Christopher, Ian Vargas, Brendon Cara, et al. "Synergizing Deep Learning-Enabled Preprocessing and Human–AI Integration for Efficient Automatic Ground Truth Generation." Bioengineering 11, no. 5 (2024): 434. http://dx.doi.org/10.3390/bioengineering11050434.

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The progress of incorporating deep learning in the field of medical image interpretation has been greatly hindered due to the tremendous cost and time associated with generating ground truth for supervised machine learning, alongside concerns about the inconsistent quality of images acquired. Active learning offers a potential solution to these problems of expanding dataset ground truth by algorithmically choosing the most informative samples for ground truth labeling. Still, this effort incurs the costs of human labeling, which needs minimization. Furthermore, automatic labeling approaches em

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Fridman, M. V., A. A. Kosareva, E. V. Snezhko, P. V. Kamlach, and V. A. Kovalev. "Papillary thyroid carcinoma whole-slide images as a basis for deep learning." Informatics 20, no. 2 (2023): 28–38. http://dx.doi.org/10.37661/1816-0301-2023-20-2-28-38.

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Objectives. Morphological analysis of papillary thyroid cancer is a cornerstone for further treatment planning. Traditional and neural network methods of extracting parts of images are used to automate the analysis. It is necessary to prepare a set of data for teaching neural networks to develop a system of similar anatomical region in the histopathological image. Authors discuss the second selection of signs for the marking of histological images, methodological approaches to dissect whole-slide images, how to prepare raw data for a future analysis. The influence of the representative size of

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Khalbuss, Walid E., Jackie Cuda, and Ioan C. Cucoranu. "Screening and dotting virtual slides: A new challenge for cytotechnologists." CytoJournal 10 (October 29, 2013): 22. http://dx.doi.org/10.4103/1742-6413.120790.

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Digital images are increasingly being used in cytopathology. Whole-slide imaging (WSI) is a digital imaging modality that uses computerized technology to scan and convert entire cytology glass slides into digital images that can be viewed on a digital display using the image viewer software. Digital image acquisition of cytology glass slides has improved significantly over the years due to the use of liquid-based preparations and advances in WSI scanning technology such as automatic multipoint pre-scan focus technology or z-stack scanning technology. Screening cytotechnologists are responsible

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Hashimoto, Noriaki, Masahiro Yamaguchi, Yukako Yagi, PinkyA Bautista, and Nagaaki Ohyama. "Referenceless image quality evaluation for whole slide imaging." Journal of Pathology Informatics 3, no. 1 (2012): 9. http://dx.doi.org/10.4103/2153-3539.93891.

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ROSZKOWIAK, L., A. KORZYNSKA, J. ZAK, D. PIJANOWSKA, Z. SWIDERSKA-CHADAJ, and T. MARKIEWICZ. "Survey: interpolation methods for whole slide image processing." Journal of Microscopy 265, no. 2 (2016): 148–58. http://dx.doi.org/10.1111/jmi.12477.

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Mukherjee, Lopamudra, Adib Keikhosravi, Dat Bui, and Kevin W. Eliceiri. "Convolutional neural networks for whole slide image superresolution." Biomedical Optics Express 9, no. 11 (2018): 5368. http://dx.doi.org/10.1364/boe.9.005368.

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Lézoray, Olivier, Metin Gurcan, Ali Can, and Jean-Christophe Olivo-Marin. "Special issue on whole slide microscopic image processing." Computerized Medical Imaging and Graphics 35, no. 7-8 (2011): 493–95. http://dx.doi.org/10.1016/j.compmedimag.2011.06.008.

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23

Chen, Pingjun, and Lin Yang. "tissueloc: Whole slide digital pathology image tissue localization." Journal of Open Source Software 4, no. 33 (2019): 1148. http://dx.doi.org/10.21105/joss.01148.

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Mu, Youqing, H. R. Tizhoosh, Taher Dehkharghanian, and Clinton J. V. Campbell. "Whole slide image representation in bone marrow cytology." Computers in Biology and Medicine 166 (November 2023): 107530. http://dx.doi.org/10.1016/j.compbiomed.2023.107530.

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Stritt, Manuel, Anna K. Stalder, and Enrico Vezzali. "Orbit Image Analysis: An open-source whole slide image analysis tool." PLOS Computational Biology 16, no. 2 (2020): e1007313. http://dx.doi.org/10.1371/journal.pcbi.1007313.

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26

Yagi, Yukako, and John R. Gilbertson. "A relationship between slide quality and image quality in whole slide imaging (WSI)." Diagnostic Pathology 3, Suppl 1 (2008): S12. http://dx.doi.org/10.1186/1746-1596-3-s1-s12.

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Sharma, Anurag, Pinky Bautista, and Yukako Yagi. "Balancing Image Quality and Compression Factor for Special Stains Whole Slide Images." Analytical Cellular Pathology 35, no. 2 (2012): 101–6. http://dx.doi.org/10.1155/2012/960684.

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The objective is to find a practical balance between quality and performance for daily high volume whole slide imaging. We evaluated whole slide images created by various scanners at different compression factors to determine the best suitable quality factor (QF) needed for pathological images of special stains.Method: We scanned two sets of eight special stains slides each at 0.50 μm/pixel resolution in Hamamatsu scanner at six and fiveQFlevels respectively to generate 72 images which were observed at a calibrated monitor by imaging specialists, a histo-technician, and a pathologist to find t

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Liu, Dehua, Chengming Li, Xiping Hu, and Bin Hu. "Dual-Attention Multiple Instance Learning Framework for Pathology Whole-Slide Image Classification." Electronics 13, no. 22 (2024): 4445. http://dx.doi.org/10.3390/electronics13224445.

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Conventional methods for tumor diagnosis suffer from two inherent limitations: they are time-consuming and subjective. Computer-aided diagnosis (CAD) is an important approach for addressing these limitations. Pathology whole-slide images (WSIs) are high-resolution tissue images that have made significant contributions to cancer diagnosis and prognosis assessment. Due to the complexity of WSIs and the availability of only slide-level labels, multiple instance learning (MIL) has become the primary framework for WSI classification. However, most MIL methods fail to capture the interdependence amo

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Stanley, RJoe, Sudhir Sornapudi, Ravitej Addanki, et al. "Automated cervical digitized histology whole-slide image analysis toolbox." Journal of Pathology Informatics 12, no. 1 (2021): 26. http://dx.doi.org/10.4103/jpi.jpi_52_20.

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Hoque, Md Ziaul, Anja Keskinarkaus, Pia Nyberg, Taneli Mattila, and Tapio Seppänen. "Whole slide image registration via multi-stained feature matching." Computers in Biology and Medicine 144 (May 2022): 105301. http://dx.doi.org/10.1016/j.compbiomed.2022.105301.

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Zheng, Yushan, Zhiguo Jiang, Haopeng Zhang, et al. "Histopathological Whole Slide Image Analysis Using Context-Based CBIR." IEEE Transactions on Medical Imaging 37, no. 7 (2018): 1641–52. http://dx.doi.org/10.1109/tmi.2018.2796130.

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Yang, Yuechen, Yu Wang, Tianyuan Yao, et al. "PySpatial: A high-speed whole slide image pathomics toolkit." Electronic Imaging 37, no. 12 (2025): 177–1. https://doi.org/10.2352/ei.2025.37.12.hpci-177.

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Baidoshvili, Alexi, Nikolas Stathonikos, Gerard Freling, et al. "Validation of a whole-slide image-based teleconsultation network." Histopathology 73, no. 5 (2018): 777–83. http://dx.doi.org/10.1111/his.13673.

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Norgan, Andrew Paul, Kabeer Kevin Shah, Justin Eddie Juskewitch, and Joseph John Maleszewski. "Open-Source Whole Slide Image Preparation and Viewing Pipeline." Archives of Pathology & Laboratory Medicine 142, no. 12 (2018): 1454–55. http://dx.doi.org/10.5858/arpa.2018-0323-le.

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Yagi, Yukako, Shigeatsu Yoshioka, Hiroshi Kyusojin, et al. "An Ultra-High Speed Whole Slide Image Viewing System." Analytical Cellular Pathology 35, no. 1 (2012): 65–73. http://dx.doi.org/10.1155/2012/626025.

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Background: One of the goals for a Whole Slide Imaging (WSI) system is implementation in the clinical practice of pathology. One of the unresolved problems in accomplishing this goal is the speed of the entire process, i.e., from viewing the slides through making the final diagnosis. Most users are not satisfied with the correct viewing speeds of available systems. We have evaluated a new WSI viewing station and tool that focuses on speed.Method: A prototype WSI viewer based on PlayStation®3 with wireless controllers was evaluated at the Department of Pathology at MGH for the following reasons

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Ma, Yibing, Zhiguo Jiang, Haopeng Zhang, et al. "Generating region proposals for histopathological whole slide image retrieval." Computer Methods and Programs in Biomedicine 159 (June 2018): 1–10. http://dx.doi.org/10.1016/j.cmpb.2018.02.020.

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Feng, Ming, Kele Xu, Nanhui Wu, et al. "Trusted multi-scale classification framework for whole slide image." Biomedical Signal Processing and Control 89 (March 2024): 105790. http://dx.doi.org/10.1016/j.bspc.2023.105790.

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Abdul Salam, Anum, Muhammad Zeeshan Asaf, Muhammad Usman Akram, et al. "Skin whole slide image segmentation using lightweight-pruned transformer." Biomedical Signal Processing and Control 106 (August 2025): 107624. https://doi.org/10.1016/j.bspc.2025.107624.

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Kovalev, V., Y. Diachenko, V. Malyshev, et al. "COMPARATIVE FEATURES OF OPEN SOURCE SOFTWARE PRODUCTS FOR THE DEVELOPMENT OF AN AUTOMATED BREAST CANCER DIAGNOSTIC PROGRAM." Eastern Ukrainian Medical Journal 7, no. 4 (2019): 377–85. http://dx.doi.org/10.21272/eumj.2019;7(4):377-385.

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Breast cancer is one of the most common cancer diseases in the world among women. The reliability of histological verification of breast cancer depends on pathologist’s experience, knowledge, his willingness to self-improve and study specialized literature. Digital pathology is also widely used for educational purposes, in telepathology, teleconsultation and research projects. Recently developed Whole Slide Image (WSI) system opens great opportunities in the histopathological diagnosis quality improvement. Digital whole-slide images provide the effective use of morphometry and various imaging

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Berman, Adam G., William R. Orchard, Marcel Gehrung, and Florian Markowetz. "SliDL: A toolbox for processing whole-slide images in deep learning." PLOS ONE 18, no. 8 (2023): e0289499. http://dx.doi.org/10.1371/journal.pone.0289499.

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The inspection of stained tissue slides by pathologists is essential for the early detection, diagnosis and monitoring of disease. Recently, deep learning methods for the analysis of whole-slide images (WSIs) have shown excellent performance on these tasks, and have the potential to substantially reduce the workload of pathologists. However, WSIs present a number of unique challenges for analysis, requiring special consideration of image annotations, slide and image artefacts, and evaluation of WSI-trained model performance. Here we introduce SliDL, a Python library for performing pre- and pos

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Aeffner, Famke, Hibret A. Adissu, Michael C. Boyle, et al. "Digital Microscopy, Image Analysis, and Virtual Slide Repository." ILAR Journal 59, no. 1 (2018): 66–79. http://dx.doi.org/10.1093/ilar/ily007.

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Abstract Advancements in technology and digitization have ushered in novel ways of enhancing tissue-based research via digital microscopy and image analysis. Whole slide imaging scanners enable digitization of histology slides to be stored in virtual slide repositories and to be viewed via computers instead of microscopes. Easier and faster sharing of histologic images for teaching and consultation, improved storage and preservation of quality of stained slides, and annotation of features of interest in the digital slides are just a few of the advantages of this technology. Combined with the d

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Lindman, Karin, JeróminoF Rose, Martin Lindvall, Claes Lundstrom, and Darren Treanor. "Annotations, ontologies, and whole slide images – Development of an annotated ontology-driven whole slide image library of normal and abnormal human tissue." Journal of Pathology Informatics 10, no. 1 (2019): 22. http://dx.doi.org/10.4103/jpi.jpi_81_18.

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Rydell, Christopher, and Joakim Lindblad. "CytoBrowser: a browser-based collaborative annotation platform for whole slide images." F1000Research 10 (March 22, 2021): 226. http://dx.doi.org/10.12688/f1000research.51916.1.

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We present CytoBrowser, an open-source (GPLv3) JavaScript and Node.js driven environment for fast and accessible collaborative online visualization, assessment, and annotation of very large microscopy images, including, but not limited to, z-stacks (focus stacks) of cytology or histology whole slide images. CytoBrowser provides a web-based viewer for high-resolution zoomable images and facilitates easy remote collaboration, with options for joint-view visualization and simultaneous collaborative annotation of very large datasets. It delivers a unique combination of functionalities not found in

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Zheng, Haixia, Yu Zhou, and Xin Huang. "Spatiality Sensitive Learning for Cancer Metastasis Detection in Whole-Slide Images." Mathematics 10, no. 15 (2022): 2657. http://dx.doi.org/10.3390/math10152657.

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Metastasis detection in lymph nodes via microscopic examination of histopathological images is one of the most crucial diagnostic procedures for breast cancer staging. The manual analysis is extremely labor-intensive and time-consuming because of complexities and diversities of histopathology images. Deep learning has been utilized in automatic cancer metastasis detection in recent years. Due to the huge size of whole-slide images, most existing approaches split each image into smaller patches and simply treat these patches independently, which ignores the spatial correlations among them. To s

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Ding, Tao, Kaijie Wu, Hao Cheng, and Chaocheng Gu. "Correction of uneven illumination in microscopic image through robust brightness distribution estimation and deviation rectification." Journal of Physics: Conference Series 2700, no. 1 (2024): 012003. http://dx.doi.org/10.1088/1742-6596/2700/1/012003.

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Abstract For diagnostic purposes, the automatic microscopic scanning system can scan and stitch multiple slide images together to produce a Whole Slide Image. This process provides a clear, high-resolution picture of the slide sample under the high-power objective lens of the microscope, enabling accurate diagnosis and analysis. However, uneven illumination affects every image acquired by a microscope, resulting in the existence of artifacts. In this paper, a novel retrospective approach based on robust brightness distribution estimation and deviation rectification is proposed. Least squares e

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Banavar, Spoorthi Ravi, Prashanthi Chippagiri, Rohit Pandurangappa, Saileela Annavajjula, and Premalatha Bidadi Rajashekaraiah. "Image Montaging for Creating a Virtual Pathology Slide: An Innovative and Economical Tool to Obtain a Whole Slide Image." Analytical Cellular Pathology 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/9084909.

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Background. Microscopes are omnipresent throughout the field of biological research. With microscopes one can see in detail what is going on at the cellular level in tissues. Though it is a ubiquitous tool, the limitation is that with high magnification there is a small field of view. It is often advantageous to see an entire sample at high magnification. Over the years technological advancements in optics have helped to provide solutions to this limitation of microscopes by creating the so-called dedicated “slide scanners” which can provide a “whole slide digital image.” These scanners can pr

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Popovici, Vlad, Aleš Křenek, and Eva Budinská. "Identification of “BRAF-Positive” Cases Based on Whole-Slide Image Analysis." BioMed Research International 2017 (2017): 1–7. http://dx.doi.org/10.1155/2017/3926498.

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A key requirement for precision medicine is the accurate identification of patients that would respond to a specific treatment or those that represent a high-risk group, and a plethora of molecular biomarkers have been proposed for this purpose during the last decade. Their application in clinical settings, however, is not always straightforward due to relatively high costs of some tests, limited availability of the biological material and time, and procedural constraints. Hence, there is an increasing interest in constructing tissue-based surrogate biomarkers that could be applied with minima

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Bándi, Péter, Maschenka Balkenhol, Bram van Ginneken, Jeroen van der Laak, and Geert Litjens. "Resolution-agnostic tissue segmentation in whole-slide histopathology images with convolutional neural networks." PeerJ 7 (December 17, 2019): e8242. http://dx.doi.org/10.7717/peerj.8242.

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Modern pathology diagnostics is being driven toward large scale digitization of microscopic tissue sections. A prerequisite for its safe implementation is the guarantee that all tissue present on a glass slide can also be found back in the digital image. Whole-slide scanners perform a tissue segmentation in a low resolution overview image to prevent inefficient high-resolution scanning of empty background areas. However, currently applied algorithms can fail in detecting all tissue regions. In this study, we developed convolutional neural networks to distinguish tissue from background. We coll

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Ruan, Jun, Zhikui Zhu, Chenchen Wu, Guanglu Ye, Jingfan Zhou, and Junqiu Yue. "A fast and effective detection framework for whole-slide histopathology image analysis." PLOS ONE 16, no. 5 (2021): e0251521. http://dx.doi.org/10.1371/journal.pone.0251521.

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Pathologists generally pan, focus, zoom and scan tissue biopsies either under microscopes or on digital images for diagnosis. With the rapid development of whole-slide digital scanners for histopathology, computer-assisted digital pathology image analysis has attracted increasing clinical attention. Thus, the working style of pathologists is also beginning to change. Computer-assisted image analysis systems have been developed to help pathologists perform basic examinations. This paper presents a novel lightweight detection framework for automatic tumor detection in whole-slide histopathology

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Vulli, Adarsh, Parvathaneni Naga Srinivasu, Madipally Sai Krishna Sashank, Jana Shafi, Jaeyoung Choi, and Muhammad Fazal Ijaz. "Fine-Tuned DenseNet-169 for Breast Cancer Metastasis Prediction Using FastAI and 1-Cycle Policy." Sensors 22, no. 8 (2022): 2988. http://dx.doi.org/10.3390/s22082988.

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Lymph node metastasis in breast cancer may be accurately predicted using a DenseNet-169 model. However, the current system for identifying metastases in a lymph node is manual and tedious. A pathologist well-versed with the process of detection and characterization of lymph nodes goes through hours investigating histological slides. Furthermore, because of the massive size of most whole-slide images (WSI), it is wise to divide a slide into batches of small image patches and apply methods independently on each patch. The present work introduces a novel method for the automated diagnosis and det

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