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Artículos de revistas sobre el tema "Histopathologie digitale"

Autor: Grafiati
Publicado: 23 de noviembre de 2024

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1

Braun, Stephan A., and Doris Helbig. "Infantile digitale Fibromatose: ein seltener myofibrozytärer Tumor mit charakteristischer Histopathologie." JDDG: Journal der Deutschen Dermatologischen Gesellschaft 12, no. 12 (2014): 1141–42. http://dx.doi.org/10.1111/ddg.12450_suppl.

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2

Cummins, Donna M., Iskander H. Chaudhry, and Matthew Harries. "Scarring Alopecias: Pathology and an Update on Digital Developments." Biomedicines 9, no. 12 (2021): 1755. http://dx.doi.org/10.3390/biomedicines9121755.

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Primary cicatricial alopecias (PCA) represent a challenging group of disorders that result in irreversible hair loss from the destruction and fibrosis of hair follicles. Scalp skin biopsies are considered essential in investigating these conditions. Unfortunately, the recognised complexity of histopathologic interpretation is compounded by inadequate sampling and inappropriate laboratory processing. By sharing our successes in developing the communication pathway between the clinician, laboratory and histopathologist, we hope to mitigate some of the difficulties that can arise in managing thes
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3

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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4

Amgad Mohamed Khater, Nesma. "Review on Advancements in Histopathology Education through Virtual Labs, Digital Microscopy and AI." International Journal of Science and Research (IJSR) 13, no. 11 (2024): 807–8. http://dx.doi.org/10.21275/sr241113034953.

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5

Min, Eunjung, Nurbolat Aimakov, Sangjin Lee, et al. "Multi-contrast digital histopathology of mouse organs using quantitative phase imaging and virtual staining." Biomedical Optics Express 14, no. 5 (2023): 2068. http://dx.doi.org/10.1364/boe.484516.

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Quantitative phase imaging (QPI) has emerged as a new digital histopathologic tool as it provides structural information of conventional slide without staining process. It is also capable of imaging biological tissue sections with sub-nanometer sensitivity and classifying them using light scattering properties. Here we extend its capability further by using optical scattering properties as imaging contrast in a wide-field QPI. In our first step towards validation, QPI images of 10 major organs of a wild-type mouse have been obtained followed by H&E-stained images of the corresponding tissu
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6

Ciga, Ozan, Tony Xu, and Anne Louise Martel. "Self supervised contrastive learning for digital histopathology." Machine Learning with Applications 7 (March 2022): 100198. http://dx.doi.org/10.1016/j.mlwa.2021.100198.

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7

Amrania, Hemmel, Giuseppe Antonacci, Che-Hung Chan, et al. "Digistain: a digital staining instrument for histopathology." Optics Express 20, no. 7 (2012): 7290. http://dx.doi.org/10.1364/oe.20.007290.

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8

Huss, Ralf, and Sarah E. Coupland. "Software‐assisted decision support in digital histopathology." Journal of Pathology 250, no. 5 (2020): 685–92. http://dx.doi.org/10.1002/path.5388.

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9

Martines, Roosecelis B., Jana M. Ritter, Joy Gary, et al. "Pathology and Telepathology Methods in the Child Health and Mortality Prevention Surveillance Network." Clinical Infectious Diseases 69, Supplement_4 (2019): S322—S332. http://dx.doi.org/10.1093/cid/ciz579.

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Abstract This manuscript describes the Child Health and Mortality Prevention Surveillance (CHAMPS) network approach to pathologic evaluation of minimally invasive tissue sampling (MITS) specimens, including guidelines for histopathologic examination and further diagnostics with special stains, immunohistochemistry, and molecular testing, as performed at the CHAMPS Central Pathology Laboratory (CPL) at the Centers for Disease Control and Prevention, as well as techniques for virtual discussion of these cases (telepathology) with CHAMPS surveillance locations. Based on review of MITS from the ea
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10

Mungenast, Felicitas, Achala Fernando, Robert Nica, et al. "Next-Generation Digital Histopathology of the Tumor Microenvironment." Genes 12, no. 4 (2021): 538. http://dx.doi.org/10.3390/genes12040538.

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Progress in cancer research is substantially dependent on innovative technologies that permit a concerted analysis of the tumor microenvironment and the cellular phenotypes resulting from somatic mutations and post-translational modifications. In view of a large number of genes, multiplied by differential splicing as well as post-translational protein modifications, the ability to identify and quantify the actual phenotypes of individual cell populations in situ, i.e., in their tissue environment, has become a prerequisite for understanding tumorigenesis and cancer progression. The need for qu
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11

Schnell, Martin, Shachi Mittal, Kianoush Falahkheirkhah, et al. "All-digital histopathology by infrared-optical hybrid microscopy." Proceedings of the National Academy of Sciences 117, no. 7 (2020): 3388–96. http://dx.doi.org/10.1073/pnas.1912400117.

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Optical microscopy for biomedical samples requires expertise in staining to visualize structure and composition. Midinfrared (mid-IR) spectroscopic imaging offers label-free molecular recording and virtual staining by probing fundamental vibrational modes of molecular components. This quantitative signal can be combined with machine learning to enable microscopy in diverse fields from cancer diagnoses to forensics. However, absorption of IR light by common optical imaging components makes mid-IR light incompatible with modern optical microscopy and almost all biomedical research and clinical w
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12

Asbeutah, Akram M., Nouralhuda Karmani, AbdulAziz A. Asbeutah, Yasmin A. Echreshzadeh, Abdullah A. AlMajran, and Khalid H. Al-Khalifah. "Comparison of Digital Breast Tomosynthesis and Digital Mammography for Detection of Breast Cancer in Kuwaiti Women." Medical Principles and Practice 28, no. 1 (2018): 10–15. http://dx.doi.org/10.1159/000495753.

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Objective: To investigate the sensitivity and specificity of digital mammography (DM) and digital breast tomosynthesis (DBT) for the detection of breast cancer in comparison to histopathology findings. Subjects and Methods: We included 65 breast lesions in 58 women, each detected by two diagnostic mammography techniques – DM and DBT using Senographe Essential (GE Healthcare, Buc, France) – and subsequently confirmed by histopathology. The Breast Imaging Reporting and Data System was used for characterizing the lesions. Results: The average age of women was 48.3 years (range 26–81 years). There
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13

Orr, Brent A., Zahangir Alom, and Quyhn T. Tran. "PATH-13. LEARNED RESIZING WITH EFFICIENT TRAINING (LRET) FACILITATES IMPROVED PERFORMANCE OF LARGE-SCALE BRAIN TUMOR HISTOLOGY IMAGE CLASSIFICATION MODELS." Neuro-Oncology 26, Supplement_4 (2024): 0. http://dx.doi.org/10.1093/neuonc/noae064.716.

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Abstract BACKGROUND Histologic examination is vital in oncology research and diagnostics. The adoption of digital scanning of whole slide images (WSI) has created an opportunity to leverage deep learning-based image classification methods to enhance diagnosis and risk stratification. However, technical limitations prevent training and deployment of accurate comprehensive multiclass deep convolutional neural networks (DCNN) models for histopathology image classification. The input dimensions of DCNN architectures are small compared to the typical pathologist field of view, degrading performance
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14

Rivenson, Yair, Kevin de Haan, W. Dean Wallace, and Aydogan Ozcan. "Emerging Advances to Transform Histopathology Using Virtual Staining." BME Frontiers 2020 (August 25, 2020): 1–11. http://dx.doi.org/10.34133/2020/9647163.

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In an age where digitization is widespread in clinical and preclinical workflows, pathology is still predominantly practiced by microscopic evaluation of stained tissue specimens affixed on glass slides. Over the last decade, new high throughput digital scanning microscopes have ushered in the era of digital pathology that, along with recent advances in machine vision, have opened up new possibilities for Computer-Aided-Diagnoses. Despite these advances, the high infrastructural costs related to digital pathology and the perception that the digitization process is an additional and nondirectly
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15

Sperandio, CP, and DJ McCarthy. "Digital arterial embolism-true blue toe syndrome. A histopathologic analysis." Journal of the American Podiatric Medical Association 78, no. 11 (1988): 593–98. http://dx.doi.org/10.7547/87507315-78-11-593.

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16

Bandyopadhyay, Samir. "Analysis of digital histopathology images for breast cancer diagnosis." International Medicine 1, no. 2 (2019): 90. http://dx.doi.org/10.5455/im.41366.

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17

ARAÚJO, ANNA LUÍZA DAMACENO, GLEYSON KLEBER DO AMARAL-SILVA, FELIPE PAIVA FONSECA, et al. "VALIDATION OF DIGITAL MICROSCOPY IN THE HISTOPATHOLOGIC DIAGNOSES OF ORAL DISEASES." Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology 129, no. 1 (2020): e146. http://dx.doi.org/10.1016/j.oooo.2019.06.631.

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18

Retamero, Juan Antonio, Jose Aneiros-Fernandez, and Raimundo G. del Moral. "Complete Digital Pathology for Routine Histopathology Diagnosis in a Multicenter Hospital Network." Archives of Pathology & Laboratory Medicine 144, no. 2 (2019): 221–28. http://dx.doi.org/10.5858/arpa.2018-0541-oa.

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Context.— Complete digital pathology and whole slide imaging for routine histopathology diagnosis is currently in use in few laboratories worldwide. Granada University Hospitals, Spain, which comprises 4 hospitals, adopted full digital pathology for primary histopathology diagnosis in 2016. Objective.— To describe the methodology adopted and the resulting experience at Granada University Hospitals in transitioning to full digital diagnosis. Design.— All histopathology glass slides generated for routine diagnosis were digitized at ×40 using the Philips IntelliSite Pathology Solution, which incl
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19

Sauter, Daniel, Georg Lodde, Felix Nensa, Dirk Schadendorf, Elisabeth Livingstone, and Markus Kukuk. "A Systematic Comparison of Task Adaptation Techniques for Digital Histopathology." Bioengineering 11, no. 1 (2023): 19. http://dx.doi.org/10.3390/bioengineering11010019.

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Due to an insufficient amount of image annotation, artificial intelligence in computational histopathology usually relies on fine-tuning pre-trained neural networks. While vanilla fine-tuning has shown to be effective, research on computer vision has recently proposed improved algorithms, promising better accuracy. While initial studies have demonstrated the benefits of these algorithms for medical AI, in particular for radiology, there is no empirical evidence for improved accuracy in histopathology. Therefore, based on the ConvNeXt architecture, our study performs a systematic comparison of
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20

Mayerich, David, Michael J. Walsh, Andre Kadjacsy-Balla, Partha S. Ray, Stephen M. Hewitt, and Rohit Bhargava. "Stain-less staining for computed histopathology." TECHNOLOGY 03, no. 01 (2015): 27–31. http://dx.doi.org/10.1142/s2339547815200010.

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Dyes such as hematoxylin and eosin (H&E) and immunohistochemical stains have been increasingly used to visualize tissue composition in research and clinical practice. We present an alternative approach to obtain the same information using stain-free chemical imaging. Relying on Fourier transform infrared (FT-IR) spectroscopic imaging and computation, stainless computed histopathology can enable a rapid, digital, quantitative and non-perturbing visualization of morphology and multiple molecular epitopes simultaneously in a variety of research and clinical pathology applications.
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21

Kumar, K. Jagadish, Subramanian Ramaswamy, and Karen Saldana. "Cutaneous Polyarteritis Nodosa Presenting with Digital Gangrene." Journal of Nepal Paediatric Society 36, no. 1 (2016): 82–84. http://dx.doi.org/10.3126/jnps.v36i1.14481.

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Cutaneous polyarteritis nodosa is a vasculitis with characteristic manifestations like tender subcutaneous nodules, livedo reticularis, cutaneous ulcers and necrosis. Diagnosis requires histopathologic evidence of necrotizing inflammation of the medium and small arteries. We report a six year old girl with cutaneous PAN with gangenous changes of the fingertips which responded to methylprednisolone. J Nepal Paediatr Soc 2016;36(1):82-84.
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22

Okuno, Taeko, Conrad Wall, and Isamu Sando. "Computerized Data Bank System for Temporal Bone Histopathology." Annals of Otology, Rhinology & Laryngology 97, no. 2 (1988): 195–98. http://dx.doi.org/10.1177/000348948809700219.

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A computerized data bank system to store and analyze temporal bone histopathologic data is described. This system uses the University of Pittsburgh's Digital Equipment Corporation System 10 computer and the System 1022 data base management software. Data on histology cases are divided into five files: General information, otologic information, summary, histopathologic information about the external ear and middle ear, and histopathologic information about the inner ear. Eleven general terms are used to describe pathologic findings, surgery, postmortem degeneration, and artifacts. In addition,
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23

Sauter, Daniel, Georg Lodde, Felix Nensa, Dirk Schadendorf, Elisabeth Livingstone, and Markus Kukuk. "Validating Automatic Concept-Based Explanations for AI-Based Digital Histopathology." Sensors 22, no. 14 (2022): 5346. http://dx.doi.org/10.3390/s22145346.

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Digital histopathology poses several challenges such as label noise, class imbalance, limited availability of labelled data, and several latent biases to deep learning, negatively influencing transparency, reproducibility, and classification performance. In particular, biases are well known to cause poor generalization. Proposed tools from explainable artificial intelligence (XAI), bias detection, and bias discovery suffer from technical challenges, complexity, unintuitive usage, inherent biases, or a semantic gap. A promising XAI method, not studied in the context of digital histopathology is
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24

Braun, Stephan A., and Doris Helbig. "Infantile digital Fibromatosis: a rare myofibrocytic tumor with characteristic histopathology." JDDG: Journal der Deutschen Dermatologischen Gesellschaft 12, no. 12 (2014): 1141–42. http://dx.doi.org/10.1111/ddg.12450.

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25

Ethunandan, M., and I. P. Downie. "Digital photographs of excised lesions: An aid to histopathology reports." British Journal of Oral and Maxillofacial Surgery 46, no. 3 (2008): 251–52. http://dx.doi.org/10.1016/j.bjoms.2007.08.001.

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26

Brown, Peter J., Debra Fews, and Nick J. Bell. "Teaching Veterinary Histopathology: A Comparison of Microscopy and Digital Slides." Journal of Veterinary Medical Education 43, no. 1 (2016): 13–20. http://dx.doi.org/10.3138/jvme.0315-035r1.

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27

Kwak, Jin Tae, Sandeep Sankineni, Sheng Xu, et al. "Correlation of magnetic resonance imaging with digital histopathology in prostate." International Journal of Computer Assisted Radiology and Surgery 11, no. 4 (2015): 657–66. http://dx.doi.org/10.1007/s11548-015-1287-x.

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28

Vallez, Noelia, Jose Luis Espinosa-Aranda, Anibal Pedraza, Oscar Deniz, and Gloria Bueno. "Deep Learning within a DICOM WSI Viewer for Histopathology." Applied Sciences 13, no. 17 (2023): 9527. http://dx.doi.org/10.3390/app13179527.

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Microscopy scanners and artificial intelligence (AI) techniques have facilitated remarkable advancements in biomedicine. Incorporating these advancements into clinical practice is, however, hampered by the variety of digital file formats used, which poses a significant challenge for data processing. Open-source and commercial software solutions have attempted to address proprietary formats, but they fall short of providing comprehensive access to vital clinical information beyond image pixel data. The proliferation of competing proprietary formats makes the lack of interoperability even worse.
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29

Thom, Leonie K., Roy R. Pool, and Richard Malik. "Digital flexor musculotendinous contracture in two Devon Rex cats." Journal of Feline Medicine and Surgery 19, no. 3 (2017): 304–10. http://dx.doi.org/10.1177/1098612x17693503.

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Clinical summary: A 13-year-old, spayed Devon Rex with unilateral digital flexor musculotendinous contracture of the forelimb was treated by surgical tenotomy. The condition improved transiently, but recurred rapidly and became bilateral. Histopathologic analysis of necropsy tissues resulted in a morphologic diagnosis of fibromyositis of the antebrachial muscles causing contracture and flexural deformity of the carpi and phalanges of both thoracic limbs. A search for similar cases yielded the clinical notes of a second cat, a 10-year-old, spayed Devon Rex, also with bilateral disease. This sec
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30

Clarke, G. M., S. Eidt, L. Sun, G. Mawdsley, J. T. Zubovits, and M. J. Yaffe. "Whole-specimen histopathology: a method to produce whole-mount breast serial sections for 3-D digital histopathology imaging." Histopathology 50, no. 2 (2007): 232–42. http://dx.doi.org/10.1111/j.1365-2559.2006.02561.x.

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31

Van Bockstal, Mieke R., Martine Berlière, Francois P. Duhoux, and Christine Galant. "Interobserver Variability in Ductal Carcinoma In Situ of the Breast." American Journal of Clinical Pathology 154, no. 5 (2020): 596–609. http://dx.doi.org/10.1093/ajcp/aqaa077.

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Abstract Objectives Since most patients with ductal carcinoma in situ (DCIS) of the breast are treated upon diagnosis, evidence on its natural progression to invasive carcinoma is limited. It is estimated that around half of the screen-detected DCIS lesions would have remained indolent if they had never been detected. Many patients with DCIS are therefore probably overtreated. Four ongoing randomized noninferiority trials explore active surveillance as a treatment option. Eligibility for these trials is mainly based on histopathologic features. Hence, the call for reproducible histopathologic
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32

Gherardi, Alessandro, and Alessandro Bevilacqua. "Manual Stage Acquisition and Interactive Display of Digital Slides in Histopathology." IEEE Journal of Biomedical and Health Informatics 18, no. 4 (2014): 1413–22. http://dx.doi.org/10.1109/jbhi.2013.2291998.

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33

Murtaza, Ghulam, Liyana Shuib, Ainuddin Wahid Abdul Wahab, et al. "Breast cancer classification using digital biopsy histopathology images through transfer learning." Journal of Physics: Conference Series 1339 (December 2019): 012035. http://dx.doi.org/10.1088/1742-6596/1339/1/012035.

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34

Jansen, Ilaria, Marit Lucas, C. Dilara Savci-Heijink, et al. "Histopathology: ditch the slides, because digital and 3D are on show." World Journal of Urology 36, no. 4 (2018): 549–55. http://dx.doi.org/10.1007/s00345-018-2202-1.

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35

Luong, Richard H. "Commentary: Digital histopathology in a private or commercial diagnostic veterinary laboratory." Journal of Veterinary Diagnostic Investigation 32, no. 3 (2020): 353–55. http://dx.doi.org/10.1177/1040638720919842.

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36

Wu, Yawen, Michael Cheng, Shuo Huang, et al. "Recent Advances of Deep Learning for Computational Histopathology: Principles and Applications." Cancers 14, no. 5 (2022): 1199. http://dx.doi.org/10.3390/cancers14051199.

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With the remarkable success of digital histopathology, we have witnessed a rapid expansion of the use of computational methods for the analysis of digital pathology and biopsy image patches. However, the unprecedented scale and heterogeneous patterns of histopathological images have presented critical computational bottlenecks requiring new computational histopathology tools. Recently, deep learning technology has been extremely successful in the field of computer vision, which has also boosted considerable interest in digital pathology applications. Deep learning and its extensions have opene
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37

Bassan, Paul, Miles J. Weida, Jeremy Rowlette, and Peter Gardner. "Large scale infrared imaging of tissue micro arrays (TMAs) using a tunable Quantum Cascade Laser (QCL) based microscope." Analyst 139, no. 16 (2014): 3856–59. http://dx.doi.org/10.1039/c4an00638k.

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38

Doherty, Trevor, Susan McKeever, Nebras Al-Attar, et al. "Feature fusion of Raman chemical imaging and digital histopathology using machine learning for prostate cancer detection." Analyst 146, no. 13 (2021): 4195–211. http://dx.doi.org/10.1039/d1an00075f.

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39

Mezei, Tibor, Melinda Kolcsár, András Joó, and Simona Gurzu. "Image Analysis in Histopathology and Cytopathology: From Early Days to Current Perspectives." Journal of Imaging 10, no. 10 (2024): 252. http://dx.doi.org/10.3390/jimaging10100252.

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Both pathology and cytopathology still rely on recognizing microscopical morphologic features, and image analysis plays a crucial role, enabling the identification, categorization, and characterization of different tissue types, cell populations, and disease states within microscopic images. Historically, manual methods have been the primary approach, relying on expert knowledge and experience of pathologists to interpret microscopic tissue samples. Early image analysis methods were often constrained by computational power and the complexity of biological samples. The advent of computers and d
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40

Demichelis, Francesca, Vincenzo Della Mea, Stefano Forti, Paolo Dalla Palma, and Carlo Alberto Beltrami. "Digital storage of glass slides for quality assurance in histopathology and cytopathology." Journal of Telemedicine and Telecare 8, no. 3 (2002): 138–42. http://dx.doi.org/10.1258/135763302320118979.

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41

Lee, K. J., and H. P. Soyer. "Smartphones, artificial intelligence and digital histopathology take on basal cell carcinoma diagnosis." British Journal of Dermatology 182, no. 3 (2019): 540–41. http://dx.doi.org/10.1111/bjd.18374.

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42

Demichelis, Francesca, Vincenzo Della Mea, Stefano Forti, Paolo Dalla Palma, and Carlo Alberto Beltrami. "Digital Storage of Glass Slides for Quality Assurance in Histopathology and Cytopathology." Journal of Telemedicine and Telecare 8, no. 3 (2002): 138–42. http://dx.doi.org/10.1177/1357633x0200800303.

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Proficiency testing programmes for measuring screening skills in pathology are mainly conducted using conventional glass microscope slides. However, the availability of robotic microscopes allows an entire conventional slide to be digitized. Our experiments have shown that, using a widely available robotized microscope and a PC, the image of a single field may be acquired in 2 s on average, including stage movements, autofocus and storage. Digitizing an entire slide, a fully automated procedure, takes up to 8 h. If the image of each field is compressed at an appropriate quality level (a compre
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43

Ferreira, Vera Christina Camargo de Siqueira, Elba Cristina Sá de Camargo Etchebehere, José Luiz Barbosa Bevilacqua, and Nestor de Barros. "Suspicious amorphous microcalcifications detected on full-field digital mammography: correlation with histopathology." Radiologia Brasileira 51, no. 2 (2018): 87–94. http://dx.doi.org/10.1590/0100-3984.2017.0025.

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Abstract Objective: To evaluate suspicious amorphous calcifications diagnosed on full-field digital mammography (FFDM) and establish correlations with histopathology findings. Materials and Methods: This was a retrospective study of 78 suspicious amorphous calcifications (all classified as BI-RADS® 4) detected on FFDM. Vacuum-assisted breast biopsy (VABB) was performed. The histopathological classification of VABB core samples was as follows: pB2 (benign); pB3 (uncertain malignant potential); pB4 (suspicion of malignancy); and pB5 (malignant). Treatment was recommended for pB5 lesions. To rule
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44

Kwak, Jin Tae, Sandeep Sankineni, Sheng Xu, et al. "Prostate Cancer: A Correlative Study of Multiparametric MR Imaging and Digital Histopathology." Radiology 285, no. 1 (2017): 147–56. http://dx.doi.org/10.1148/radiol.2017160906.

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45

Dessinioti, Clio, Andriani Tsiakou, Athina Christodoulou, and Alexander J. Stratigos. "Clinical and Dermoscopic Findings of Nevi after Photoepilation: A Review." Life 13, no. 9 (2023): 1832. http://dx.doi.org/10.3390/life13091832.

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Atypical clinical and dermoscopic findings, or changes in pigmented melanocytic lesions located on body areas treated with lasers or intense pulsed light (IPL) for hair removal (photoepilation), have been described in the literature. There are three prospective studies in a total of 79 individuals with 287 melanocytic nevi and several case reports reporting the dermoscopic findings and changes after photoepilation. Clinical changes have been reported in 20–100% of individuals, while dermoscopic changes have been observed in 48% to 93% of nevi. More frequent dermoscopic changes included bleachi
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46

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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Hipp, Jason, Jerome Cheng, Stephanie Daignault, et al. "Automated Area Calculation of Histopathologic Features Using SIVQ." Analytical Cellular Pathology 34, no. 5 (2011): 265–75. http://dx.doi.org/10.1155/2011/606273.

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Recently, with the advent of the 7th edition of the AJCC Cancer Staging manual, at least one set of criteria (e.g. breast) were modified to now require the measurement of maximal depth of stromal invasion. With the current manual interpretive morphological approaches typically employed by surgical pathologists to assess tumor extent, the specialty now potentially has stumbled upon a crossroads of practice, where the diagnostic criteria have exceeded the capabilities of our commonly available tools. While whole slide imaging (WSI) technology holds the potential to offer many improvements in cli
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48

Afzal, Kanza, Nadia Gul, Khalid Mehmood, Sobia Jawwad, and Bushra Iqbal. "Assessment of Diagnostic Accuracy of Digital Breast Tomosynthesis in Distinguishing Malignant and Benign Breast Lesions." Life and Science 5, no. 1 (2024): 06. http://dx.doi.org/10.37185/lns.1.1.416.

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Objective: The study aims to determine the diagnostic accuracy of digital breast tomosynthesis in diagnosing malignant and benign lesions, keeping histopathology as the gold standard.Study Design: Cross-sectional study.Place and Duration of Study: The study was carried out at the Department of Diagnostic Radiology, POF th th Hospital, Wah Cantt, Pakistan over a period of six months from 11th July 2021 to 11th January 2022.Methods: A total of 200 women presenting with suspicion of breast malignancy were selected consecutively from the outpatient department, and Digital Breast Tomosynthesis(DBT)
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49

Montague, Paul R., Margaret Meyer, and Robert Folberg. "Technique for the Digital Imaging of Histopathologic Preparations of Eyes for Research and Publication." Ophthalmology 102, no. 8 (1995): 1248–51. http://dx.doi.org/10.1016/s0161-6420(95)30882-2.

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50

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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