Relevant bibliographies by topics / Histopathologie – Innovation

Contents

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

Academic literature on the topic 'Histopathologie – Innovation'

Author: Grafiati
Published: 28 September 2024
Last updated: 31 July 2025

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Journal articles on the topic "Histopathologie – Innovation"

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Danks, Janine A., Roula Papadopoulos, and Nicholas J. Vardaxis. "Innovation in Histopathology Teaching." Journal of Histotechnology 32, no. 3 (2009): 119–21. http://dx.doi.org/10.1179/his.2009.32.3.119.

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Tacha, David E., Linda C. Bloom, and Ball Lauren R. "Histopathology Instrumentation: Innovations in the 1980s." Laboratory Medicine 18, no. 8 (1987): 519–23. http://dx.doi.org/10.1093/labmed/18.8.519.

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Naik, Paras, Jem Rashbass, Mark Bennett, Sue Cossins, and Nick R. Griffin. "IT innovation in histopathology recruitment, training and research." British Journal of Hospital Medicine 66, no. 10 (2005): 563–65. http://dx.doi.org/10.12968/hmed.2005.66.10.19893.

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Triwahyuni, Heni, and Lasmijan Lasmijan. "Inovasi Alat Penunjang Pembuatan Sediaan Histopatologi." Jurnal Pengelolaan Laboratorium Pendidikan 7, no. 2 (2025): 122–27. https://doi.org/10.14710/jplp.7.2.122-127.

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The availability of supporting tools for histopathology slide preparation remains limited, characterized by high usage rates, inefficient material consumption, cleaning difficulties, frequent damage, high costs, small capacity, and low precision. This study aims to develop prototype innovations of supporting tools for histopathology preparation to address these issues. Three types of innovative tools were developed: a paraffin block mold, a slide rack for an automatic staining machine, and a slide box made of silicone-resin. The development process consisted of three main stages: the first mol
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Bardhan, Neelkanth M., Vivek Rastogi, Rebecca L. Stone, and Angela M. Belcher. "Abstract 6166: A whole-organ ex vivo optical imaging technique for non-destructive, more precise identification of serous tubal intraepithelial carcinoma (STIC) in fallopian tubes." Cancer Research 84, no. 6_Supplement (2024): 6166. http://dx.doi.org/10.1158/1538-7445.am2024-6166.

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Abstract Objective With the recent discovery of the fallopian tube (FT) origin of the most common and lethal type of ovarian cancer, high grade serous cancer (HGSC), engineering solutions to overcome the limitations of standard histopathology to more reliably identify incipient HGSCs and their serous tubal intraepithelial carcinoma (STIC) precursors are much needed. This investigation tests the potential of whole-organ ex vivo optical imaging of freshly excised FTs to label and sample abnormal tubal epithelium prior to formalin fixation and paraffin embedding for standard histopathology. Metho
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Karthikeyan Ramalingam,. "Innovations in Oral Pathology Laboratory - A Mini Review." International Journal of Head and Neck Pathology 6, no. 2 (2023): 1–5. http://dx.doi.org/10.56501/intjheadneckpathol.v6i1.914.

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Over the past two decades, a multitude of technological advancements have been integrated into histopathology laboratories, offering tools to enhance standardization and ensure occupational safety. Digital tracking plays a central role in guiding the entire process, from labeling cassettes and slides to the final stages of generating whole slide images, and storage of tissue blocks and tissue sections. Versatile equipment has effectively replaced time-consuming manual tasks, which were susceptible to errors and material loss. Currently, collaborative robots are assuming responsibilities once e
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Ahmed, Shakil, Asadullah Shaikh, Hani Alshahrani, et al. "Transfer Learning Approach for Classification of Histopathology Whole Slide Images." Sensors 21, no. 16 (2021): 5361. http://dx.doi.org/10.3390/s21165361.

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The classification of whole slide images (WSIs) provides physicians with an accurate analysis of diseases and also helps them to treat patients effectively. The classification can be linked to further detailed analysis and diagnosis. Deep learning (DL) has made significant advances in the medical industry, including the use of magnetic resonance imaging (MRI) scans, computerized tomography (CT) scans, and electrocardiograms (ECGs) to detect life-threatening diseases, including heart disease, cancer, and brain tumors. However, more advancement in the field of pathology is needed, but the main h
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Murthy, M. S. N., M. G. Jones, J. D. Davies, et al. "Scanning confocal near-infra-red microscopy: a new microscopy technique for three-dimensional histopathology." Engineering Science & Education Journal 4, no. 5 (1995): 223–30. http://dx.doi.org/10.1049/esej:19950509.

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Hegde, Sankalp, and Bhavadharini RM. "LuCoNet: A Convolutional Neural Network Model for Lung Cancer and Colon Cancer Prediction Using Histopathological Images." International Research Journal of Multidisciplinary Scope 05, no. 03 (2024): 407–19. http://dx.doi.org/10.47857/irjms.2024.v05i03.0766.

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Histopathology is the study of cellular structures, tissues and their abnormalities to diagnose a wide range of diseases, with a primary focus on cancer. The recent innovations and advancements in image analysis techniques and machine learning enable the histopathologists to automate the process of detection and classification of diseases observed in histopathology images. Traditional visual analysis by pathologists, though skilled, is slow and prone to inconsistencies. By utilizing advanced techniques, such as Convolutional Neural Networks, this project aims to revolutionize disease classific
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Habawel, Candice Mabette, Listya Purnamasari, Joseph Peñano Olarve, and Joseph Flores dela Cruz. "Comparative Efficacy of Different Fixed Drug Combination on Clinical Signs of Respiratory Disease in Starter Pigs." Jurnal Veteriner 23, no. 3 (2022): 297–305. http://dx.doi.org/10.19087/jveteriner.2022.23.3.297.

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The high occurrence of respiratory disease in pigs has led to the innovation of fixed-dose drugcombinations (FDCs). A study was conducted to determine the efficacy of two FDCs on 15 starter pigsshowing clinical signs of respiratory disease to determine its effect on their respiratory health and growth.Treatment 1 (T1) was the control group and did not receive any medication. Treatment 2 (T2) contains 90g of Doxycycline, 40 g of Tylosin, 30 g of Paracetamol, 5 g of Bromhexine, and 500 mg of Prednisolone asactive ingredients per kilogram. Treatment 3 (T3) contains 150 g of Amoxicillin Trihydrate
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Dissertations / Theses on the topic "Histopathologie – Innovation"

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Habis, Antoine Aurélien. "Developing interactive artificial intelligence tools to assist pathologists with histology annotation." Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAT022.

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L'histopathologie et l'analyse des lames de microscope numérisées (WSI) représente un domaine très pertinent de la médecine puisque une biopsie peut dévoiler de nombreuses maladies parfois difficiles ou impossibles à diagnostiquer à l'œil nu ou avec d'autres techniques d'imagerie. Avec l'avènement de l'apprentissage profond, qui nécessite un grand nombre d'images annotées pour être efficace, la nécessité d'obtenir rapidement des annotations de qualité est devenue évidente. L'objectif de cette thèse est de développer des algorithmes d'intelligence artificielle pour des annotations et des correc
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Book chapters on the topic "Histopathologie – Innovation"

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Singh, Pushap Deep, Arnav Bhavsar, and K. K. Harinarayanan. "Histopathology Whole Slide Image Analysis for Breast Cancer Detection." In EAI/Springer Innovations in Communication and Computing. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15816-2_2.

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Tayel, Mazhar B., Mohamed-Amr A. Mokhtar, and Ahmed F. Kishk. "Breast Cancer Diagnosis Using Histopathology and Convolution Neural Network CNN Method." In International Conference on Innovative Computing and Communications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2821-5_49.

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Fridrihsone, Ilze, Ilze Strumfa, Boriss Strumfs, et al. "Thyroid Nodules in Diagnostic Pathology: From Classic Concepts to Innovations." In Histopathology - An Update. InTech, 2018. http://dx.doi.org/10.5772/intechopen.77117.

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Brosnan, Bríd, Inna Skarga-Bandurova, Tetiana Biloborodova, and Illia Skarha-Bandurov. "An Integrated Approach to Automated Diagnosis of Cervical Intraepithelial Neoplasia in Digital Histology Images." In Caring is Sharing – Exploiting the Value in Data for Health and Innovation. IOS Press, 2023. http://dx.doi.org/10.3233/shti230220.

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The study proposes an integrated approach to automated cervical intraepithelial neoplasia (CIN) diagnosis in epithelial patches extracted from digital histology images. The model ensemble, combined CNN classifier, and highest-performing fusion approach achieved an accuracy of 94.57%. This result demonstrates significant improvement over the state-of-the-art classifiers for cervical cancer histopathology images and promises further improvement in the automated diagnosis of CIN.
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Recharla, Mahesh. "The science of Alzheimer’s disease: Current research, therapies, and hope for the future." In Pioneering Biomedical Breakthroughs: Advanced Multidisciplinary Research in Neurological Disorders, Renal Diseases, and Postpartum Health through Technology and Innovation. Deep Science Publishing, 2025. https://doi.org/10.70593/978-81-988918-4-6_2.

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Alzheimer's Disease is a progressive neurodegenerative disorder that is the most common cause of cognitive impairment and dementia in older adults, with approximately 6 million people afflicted in the United States. Major risk factors include advanced age, male sex, family history, prior head trauma, and genetic factors that promote amyloid deposition. The clinical hallmarks are slow but progressive memory loss and loss of function for daily tasks, but secondary neuropsychiatric symptoms such as depression and apathy are present in the early stages and may precede memory loss. Characteristic c
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Subhy Alsheikhly, Ahmed, and Mazin Ahmed Subhy Alsheikhly. "A Comprehensive Review of Breast Cancer and the Latest Advancement in Diagnosis and Treatment." In Latest Research on Breast Cancer [Working Title]. IntechOpen, 2025. https://doi.org/10.5772/intechopen.1008946.

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Breast cancer, a significant global health challenge, remains a leading cause of cancer-related morbidity and mortality worldwide, necessitating comprehensive strategies for diagnosis, treatment, and survivorship care. Early detection through modalities such as mammography, ultrasound, MRI, and tissue sampling is crucial. Genetic testing, on the other hand, identifies hereditary risks. Tissue sampling and biopsy provide a definitive histopathologic diagnosis. Breast cancer care aims to diagnose timely and achieve optimal outcomes while preserving quality of life. Surgical approaches remain int
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Conference papers on the topic "Histopathologie – Innovation"

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Bhatt, Chandradeep, Vaibhav Kumar Kapriyal, Yash Kharola, Rama Koranga, Ishita Chhetri, and Teekam Singh. "Advanced Automation for Colorectal Tissue Classification in Histopathology." In 2024 Asia Pacific Conference on Innovation in Technology (APCIT). IEEE, 2024. http://dx.doi.org/10.1109/apcit62007.2024.10673436.

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Rathor, Kapil, Aditya Ballamwar, Sam Selvaraj, and Archana Bhise. "Performance analysis of GANs for synthetic histopathology Image generation." In 2025 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI). IEEE, 2025. https://doi.org/10.1109/iatmsi64286.2025.10985467.

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Shanto, Shakib Sadat, Ahmed Shakib Reza, Safwan Islam, et al. "From Pixels to Diagnosis: An Innovative EfficientNetB3 Approach for Interpreting Lung and Colon Cancer Histopathology." In 2024 27th International Conference on Computer and Information Technology (ICCIT). IEEE, 2024. https://doi.org/10.1109/iccit64611.2024.11022423.

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Zidoum, Hamza, ArunaDevi Karuppasamy, Mazin Abdullatif Abdulrahman Mukhtar, and Maiya Al-Bahri. "Explainable AI for Breast Cancer Diagnosis: A Convolutional Autoencoder with Modified Loss Functions and Grad-CAM Visualization for Histopathology Image Classification." In 2025 International Conference for Artificial Intelligence, Applications, Innovation and Ethics (AI2E). IEEE, 2025. https://doi.org/10.1109/ai2e64943.2025.10983136.

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T, Soumya. "Detection and Differentiation of blood cancer cells using Edge Detection method." In The International Conference on scientific innovations in Science, Technology, and Management. International Journal of Advanced Trends in Engineering and Management, 2023. http://dx.doi.org/10.59544/zbua6077/ngcesi23p138.

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Medical imaging is an essential data source that has been leveraged worldwide in health- care systems. In pathology, histopathology images are used for cancer diagnosis, whereas these images are very complex and their analyses by pathologists require large amounts of time and effort. On the other hand, although convolutional neural networks (CNNs) have produced near-human results in image processing tasks, their processing time is becoming longer and they need higher computational power. In this paper, we implement a quantized ResNet model on two histopathology image datasets to optimize the i
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Konstantinov, Andrei, and Lev Utkin. "Multiple Instance Learning through Explanation by Using a Histopathology Example." In 2022 31st Conference of Open Innovations Association (FRUCT). IEEE, 2022. http://dx.doi.org/10.23919/fruct54823.2022.9770901.

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Gopalan, Vinod, Erick Chan, Debbie Ho, and Alfred Lam. "EXPLORING MEDICAL STUDENT ENGAGEMENT, PERCEPTION AND COMPETENCY IN CLINICALLY INTEGRATED HISTOPATHOLOGY." In 10th annual International Conference of Education, Research and Innovation. IATED, 2017. http://dx.doi.org/10.21125/iceri.2017.2204.

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M. D, Tharun Kumar, Soniya Priyatharsini G., and Geetha S. "Breast Cancer Detection Using Machine Learning Classifier." In The International Conference on scientific innovations in Science, Technology, and Management. International Journal of Advanced Trends in Engineering and Management, 2023. http://dx.doi.org/10.59544/ovzf8018/ngcesi23p140.

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Breast cancer is one of the main causes of cancer death worldwide. Early diagnostics significantly increases the chances of correct treatment and survival, but this process is tedious and often leads to a disagreement between pathologists. The diagnosis is based on the qualification of histopathologist, who will look for abnormal cells. However, if the histopathologist is not well-trained, this may lead to wrong diagnosis. Computer- aided diagnosis systems showed potential for improving the diagnostic accuracy. In this work, we develop the computational approach based on deep convolution neura
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Koyun, Onur Can, and Tulay Yildirim. "Adversarial Nuclei Segmentation on H&E Stained Histopathology Images." In 2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA). IEEE, 2019. http://dx.doi.org/10.1109/inista.2019.8778369.

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Nawandhar, Archana, Navin Kumar, and Lakshmi Yamujala. "Performance Analysis of Neighborhood Component Feature Selection for Oral Histopathology Images." In 2019 PhD Colloquium on Ethically Driven Innovation and Technology for Society (PhD EDITS). IEEE, 2019. http://dx.doi.org/10.1109/phdedits47523.2019.8986921.

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