Relevant bibliographies by topics / Histopathologie – Innovation

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  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

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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 (September 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 (August 1, 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 (October 2005): 563–65. http://dx.doi.org/10.12968/hmed.2005.66.10.19893.

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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 (March 22, 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. Methods This investigation prototyped “OVASEEK”, a whole-organ, near-infrared optical imaging platform for identification of STICs. This prospective biospecimen protocol with annotated clinical data was approved by the IRB. Following salpingectomy at the time of hysterectomy for benign indications, FTs from study participants are longitudinally bivalved. Half of the FT is retained for routine evaluation using the Sectioning and Extensively Examining the Fimbriated end (SEE-FIM) protocol by Johns Hopkins gynecologic pathologists (Gyn Path), while the other half is sent overnight in organ transplant media to MIT for imaging on OVASEEK. Hyperspectral “label free” first pass imaging is performed using a series of band-pass filters. Second pass fluorescence imaging is then performed using nanoparticles tagged with anti-LAMC1 antibodies targeting laminin γ1, a known STIC surface marker. Abnormal signal(s) on OVASEEK imaging of the FT epithelium are tattooed with black ink, the tissue is formalin fixed and returned to Gyn Path for serial sectioning. Research findings are reported in an addendum to the formal pathology report in the electronic medical record and discussed with the patient by the gynecologic oncologist co-investigator. Results OVASEEK enabled non-destructive imaging over a wide field-of-view ~ 12×12 cm, with features of interest in the 1,050-1,550 nm range. In this pilot study, OVASEEK identified histopathologic abnormalities missed by standard SEE-FIM in 20% of FTs (n=2 out of 10). In each case, OVASEEK found microscopic (~200 µm) foci of salpingitis, a lymphoplasmacytic infiltrate consistent with inflammation. Performance of serial sectioning and histopathologic examination of the tattooed epithelium yielded this diagnosis. Conclusion Work is ongoing to improve the resolution, speed and sensitivity of OVASEEK for STIC detection. Identification of µm-sized foci of inflammation using OVASEEK is proof-of-principle that whole-organ ex vivo imaging of freshly excised FTs may be an innovation that improves the diagnostic performance of routine histopathology. Accurate and reproducible diagnosis of STIC and concurrent microscopic HGSC is imperative to the understanding of the early pathogenesis of HGSC in clinically actionable ways. Citation Format: Neelkanth M. Bardhan, Vivek Rastogi, Rebecca L. Stone, Angela M. Belcher. A whole-organ ex vivo optical imaging technique for non-destructive, more precise identification of serous tubal intraepithelial carcinoma (STIC) in fallopian tubes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6166.
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Karthikeyan Ramalingam,. "Innovations in Oral Pathology Laboratory - A Mini Review." International Journal of Head and Neck Pathology 6, no. 2 (October 13, 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 exclusively carried out by humans. The emergence of these novel technologies is anticipated to help in improving oral pathology laboratory practices.
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Ahmed, Shakil, Asadullah Shaikh, Hani Alshahrani, Abdullah Alghamdi, Mesfer Alrizq, Junaid Baber, and Maheen Bakhtyar. "Transfer Learning Approach for Classification of Histopathology Whole Slide Images." Sensors 21, no. 16 (August 9, 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 hurdle causing the slow progress is the shortage of large-labeled datasets of histopathology images to train the models. The Kimia Path24 dataset was particularly created for the classification and retrieval of histopathology images. It contains 23,916 histopathology patches with 24 tissue texture classes. A transfer learning-based framework is proposed and evaluated on two famous DL models, Inception-V3 and VGG-16. To improve the productivity of Inception-V3 and VGG-16, we used their pre-trained weights and concatenated these with an image vector, which is used as input for the training of the same architecture. Experiments show that the proposed innovation improves the accuracy of both famous models. The patch-to-scan accuracy of VGG-16 is improved from 0.65 to 0.77, and for the Inception-V3, it is improved from 0.74 to 0.79.
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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 classification and management in histopathology. Researchers are now using convolutional neural networks and other algorithms to accurately segment tissues, extract key features, and even predict cancer diagnosis and treatment response. These automated methods hold immense potential for faster, more precise cancer diagnosis and personalized care. The proposed model LuCoNet is a Convolution Neural Network Architecture that uses the publicly available dataset comprises 25,000 histopathological JPEG images, initially sourced from HIPAAcompliant datasets. It includes 750 lung tissue images and 500 colon tissue images, augmented to expand the dataset. This study underscores the transformative potential of deep learning in histopathology image analysis, promising enhanced diagnostic accuracy and personalized treatment strategies. The performance of LuCoNet was compared with other models evaluated in the literature survey and LuCoNet performed extremely well in prediction with 98.5%, 0.986, 0.988, and 0.984 for accuracy, Precision, Recall and F1-Score measures.
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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 (September 30, 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, 100 g ofTylosin Tartrate, and 5 g of Bromhexine Hydrochloride as active ingredients per kilogram. The treatmentgives at a therapeutic dose of 10 g/gallon of water twice a day for 7 days.The effects of FDCs were measuredthrough clinical sign evaluation, gross pathologic lung lesion scoring, histopathologic examination, andevaluation of the production performance of the starter pigs using analysis of variance (ANOVA) for aCompletely Randomized Design.. Pigs treated with Treatment 2 had better clinical evaluation scores andproduction performance than Treatment 3. Histopathologic examination demonstrated minimal tissuerepair in all FDCs studied. Improvement denotes that the treatment produces a positive effect.
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Murthy, M. S. N., M. G. Jones, J. D. Davies, P. C. Jackson, J. Kulka, P. N. T. Wells, M. Halliwell, and D. R. Bull. "Scanning confocal near-infra-red microscopy: a new microscopy technique for three-dimensional histopathology." Engineering Science & Education Journal 4, no. 5 (October 1, 1995): 223–30. http://dx.doi.org/10.1049/esej:19950509.

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Kirchhof, Nicole. "What Is “Preclinical Device Pathology”: An Introduction of the Unfamiliar." Toxicologic Pathology 47, no. 3 (February 5, 2019): 205–12. http://dx.doi.org/10.1177/0192623319827502.

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Medical device pathologists are involved in the preclinical evaluation of medical devices that will be temporarily inserted or permanently and often irreversibly implanted in the human body. The medical device industry is technology based, allowing for rapid device iterations; innovations occur at an accelerated rate compared to the innovations in the pharmaceutical industry. The device pathologist provides the pathology results and is, by training and experience, in an ideal position to help the medical engineer and innovator tackle biomedical problems and to comment on the possible and actual outcomes of preclinical studies. Device pathology expertise is typically a necessity in the prelude for regulatory submission. However, there is a lack of detailed guidelines for a comprehensive preclinical pathology evaluation of the final product after implantation in a test animal. What specifically unites device pathologists is the reliance on gross pathology as the basis for spatial context needed for appropriate histopathologic analyses, the knowledge of detailed protocol instructions, a good understanding of wound healing including the “implant trauma,” and interaction with ambitious device innovators. In this article, it is my aim to amalgamate the following articles in this issue with pertinent background information intended to be informative, critical, and stimulating.
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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 corrections interactives rapides afin de faciliter la supervision de l'utilisateur dans la segmentation d'images d'histopathologie. Cette thèse présente nos contributions en utilisant trois stratégies d'interaction ainsi que des formalismes mathématiques d'apprentissage profond sous-jacents différents. Ensemble, nos contributions couvrent un large éventail de cas d'utilisation:(1) Le premier outil est entièrement supervisé et s'attaque à la tâche de correction de la segmentation des noyaux cellulaires. Les noyaux sont des structures biologiques qui peuvent être observées distinctement à un grossissement de ×40 et qui sont essentielles pour plusieurs tâches de diagnostic. En effet, des marqueurs tels que la densité des noyaux ou le rapport entre la surface du noyau et celle du cytoplasme sont révélateurs de certaines pathologies. L'outil proposé est constitué d'une pipeline Click and Refine, exploitant de nouvelles métriques sur les similarités de patchs et de nouvelles architectures d'apprentissage pour affiner quatre types d'erreurs de segmentation, spécifiques aux noyaux.(2) Le second outil consiste en une méthode de segmentation faiblement supervisée testée sur des régions tumorales dans le cancer métastatique des ganglions lymphatiques du sein. Ces régions tumorales sont des structures biologiques clairement visibles à faible grossissement (×5 ou × 10). La première partie de notre algorithme fournit une segmentation initiale grossière de l'ensemble de la WSI basée sur des annotations partielles, qui peut ensuite être révisée à l'aide de corrections de segmentation rapides, interactives et non locales.(3) Enfin, le troisième outil propose une méthode de segmentation totalement non supervisée et une variante à apprentissage unique pour segmenter des structures biologiques hétérogènes complexes sur des WSI entières. La version d'apprentissage One-Shot est évaluée sur un ensemble de données de tubules rénaux dilatés. Les tubules dilatés sont des structures biologiques de taille moyenne qui peuvent être observées à un grossissement moyen de ×10-20. Ils sont révélateurs de certaines maladies telles que l'obstruction des voies urinaires. La méthode Deep ContourFlow proposée traduit les concepts de contours actifs en fonctions de perte différentiables exploitées dans les architectures d'apprentissage profond
Histopathology on Whole Slide Images (WSI) represents a very valuable field of medicine since the study of biopsies with microscopes can reveal several diseases that are sometimes difficult or impossible to diagnose with the naked eye or other imaging techniques. With the advent of deep learning, which requires a large number of annotated images to be effective, the need to obtain quickly high-quality annotations became clear. The purpose of this thesis is to develop artificial intelligence algorithms for fast interactive annotations and corrections to facilitate user supervision in histopathology image segmentation. This thesis presents our contributions using three different interaction strategies and underlying deep-learning mathematical formalisms. Together, our contributions cover a wide range of use cases:(1) The first tool is completely supervised and tackles the task of correcting nuclei segmentation. Nuclei are biological structures that can be observed distinctly at ×40 magnification and which are essential for several diagnosis tasks. In fact, markers such as the density of nuclei or the ratio between the area of the nucleusand that of the cytoplasm are indicative of certain conditions. The proposed tool proposes a Click and Refine pipeline, exploiting novel metrics on patch similarities and novel architecture training designs to refine four types of segmentation errors, specific to nuclei.(2) The second tool consists of a weakly supervised segmentation method tested on tumoral regions in lymph node metastatic breast cancer. These tumoral regions are biological structures clearly visible at low magnification(×5 or × 10). The first part of our algorithm provides an initial coarse segmentation of the entire WSI based on scribbles, which can then be corrected using fast interactive and non-local segmentation correction inputs.(3) Finally, the third tool proposes a completely unsupervised segmentation tool and a one-shot variant to segment complex heterogeneous biological structures on whole WSIs. The One-Shot learning version is evaluated on a dataset of kidney-dilated tubules. Dilated tubules are medium-sized biological structures that can be observed at an average magnification of ×10-20. They are indicative of some diseases such as urinary tract obstruction. The underlying proposed Deep ContourFlow method translates concepts of active contours into differentiable loss functions exploited in deep-learning architectures
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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, 31–56. Cham: 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, 585–600. Singapore: 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, Andrejs Vanags, Dainis Balodis, Arvids Jakovlevs, Arnis Abolins, and Janis Gardovskis. "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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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), 1–9. IEEE, 2024. http://dx.doi.org/10.1109/apcit62007.2024.10673436.

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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 inference power consumption
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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 neural networks for breast cancer histology image classification. Hematoxylin and eosin stained breast histology microscopy image dataset is provided as a part of the ICIAR 2018 Grand Challenge on Breast Cancer Histology Images. Our approach utilizes several deep neural network architectures. Convolutional Neural Networks for Binary class classification and multiclass classification. The Binary class classification is used to classify the cancer cells to malignant and benign. And the Multiclass classification these classes into different subclasses like adenosis, fibroadenoma, phyllodes tumour, tabular adenoma for benign class and ductal carcinoma, lobular carcinoma, mucinous carcinoma, papillary carcinoma for malignant class. The result will show Convolutional Neural Networks outperformed the handcrafted feature based classification with high accuracy in both binary and multiclass classification.
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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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Laxmisagar, H. S., and M. C. Hanumantharaju. "A Survey on Automated Detection of Breast Cancer based Histopathology Images." In 2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA). IEEE, 2020. http://dx.doi.org/10.1109/icimia48430.2020.9074915.

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Fadhlia, Fadhlia, and Elvita Nora Susana. "Unilateral Benign Thyroid Lesion Management with Histopathology Results Following Surgery Was a Malignancy." In 2nd Global Health and Innovation in conjunction with 6th ORL Head and Neck Oncology Conference (ORLHN 2021). Paris, France: Atlantis Press, 2021. http://dx.doi.org/10.2991/ahsr.k.220206.042.

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Sukweenadhi, Johan, Risma Ikawaty, Yohanes Bosko Anne Marie, Farizky Martriano Humardani, Lisa Thalia Mulyanata, Lady Theresa Adeodata Tanaya, and Sulistyo Emantoko Dwi Putra. "Changes of histopathology and PPAR-ɣ gene expression in hyperglycaemia-mice." In 12TH INTERNATIONAL SEMINAR ON NEW PARADIGM AND INNOVATION ON NATURAL SCIENCES AND ITS APPLICATIONS (12TH ISNPINSA): Contribution of Science and Technology in the Changing World. AIP Publishing, 2024. http://dx.doi.org/10.1063/5.0218053.

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