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Deep Neural Network Analysis of Pathology Images With Integrated Molecular Data for Enhanced Glioma Classification and Grading

Linmin Pei, Karra A. Jones, Zeina A. Shboul, James Y. Chen, James Y. Chen, Khan M. Iftekharuddin
2021 Frontiers in Oncology  
In this work, we propose a novel glioma analytical method that, for the first time in the literature, integrates a cellularity feature derived from the digital analysis of brain histopathology images integrated  ...  (ResNet) DNN, respectively.  ...  proposed an automated brain tumor type classification in whole-slide digital pathology images using local representative tiles (6) .  ... 
doi:10.3389/fonc.2021.668694 doaj:8c8d4546cc394d7ea8b767ccf8835e5e fatcat:7mv2levqe5evjfcd2ui5yfnrim

Artificial Intelligence for Histology-Based Detection of Microsatellite Instability and Prediction of Response to Immunotherapy in Colorectal Cancer

Lindsey A. Hildebrand, Colin J. Pierce, Michael Dennis, Munizay Paracha, Asaf Maoz
2021 Cancers  
Artificial intelligence has been used to predict MSI/dMMR directly from hematoxylin and eosin (H&E) stained tissue slides.  ...  Testing all CRC patients for MSI/dMMR is recommended as screening for Lynch Syndrome and, more recently, to determine eligibility for immune checkpoint inhibitors in advanced disease.  ...  Using the ResNet CNN, the prediction of PD-L1 expression was performed from H&E slides in non-small cell lung cancer [59] .  ... 
doi:10.3390/cancers13030391 pmid:33494280 pmcid:PMC7864494 fatcat:d5kroxk4azadxojuu76kdegm3q

Classification of Mouse Lung Metastatic Tumor with Deep Learning

Ha Neul Lee, Hong-Deok Seo, Eui-Myoung Kim, Beom Seok Han, Jin Seok Kang
2021 Biomolecules & Therapeutics  
Here, we used a Inception-v3 deep learning model to detect mouse lung metastatic tumors via whole slide imaging (WSI); we cropped the images to 151 by 151 pixels.  ...  When images from lung tissue containing tumor tissues were evaluated, the model accuracy was 98.76%. When images from normal lung tissue were evaluated, the model accuracy ("no tumor") was 99.87%.  ...  Digital pathology refers to information collected using digitized slides.  ... 
doi:10.4062/biomolther.2021.130 pmid:34725310 pmcid:PMC8902456 fatcat:as3ksyzpxff47kzwkwvy62yl44

Direct Cellularity Estimation on Breast Cancer Histopathology Images Using Transfer Learning

Ziang Pei, Shuangliang Cao, Lijun Lu, Wufan Chen
2019 Computational and Mathematical Methods in Medicine  
In the workflow of RCB assessment, estimation of cancer cellularity is a critical task, which is conventionally achieved by manually reviewing the hematoxylin and eosin- (H&E-) stained microscopic slides  ...  In this work, we develop an automatic and direct method to estimate cellularity from histopathological image patches using deep feature representation, tree boosting, and support vector machine (SVM),  ...  Acknowledgments e data used in this research were acquired from Sunnybrook Health Sciences Centre with funding from the Canadian Cancer Society and were made available for the BreastPathQ challenge, sponsored  ... 
doi:10.1155/2019/3041250 pmid:31281408 pmcid:PMC6590493 fatcat:wiv6mofz7fcrfilfl3ppwkm6y4

SPIE-AAPM-NCI BreastPathQ challenge: an image analysis challenge for quantitative tumor cellularity assessment in breast cancer histology images following neoadjuvant treatment

Nicholas Petrick, Shazia Akbar, Kenny H. Cha, Sharon Nofech-Mozes, Berkman Sahiner, Marios A. Gavrielides, Jayashree Kalpathy-Cramer, Karen Drukker, Anne L. Martel, for the BreastPathQ Challenge Group
2021 Journal of Medical Imaging  
The task of the BreastPathQ challenge was computerized estimation of tumor cellularity (TC) in breast cancer histology images following neoadjuvant treatment.  ...  The summary performance metric used for comparing and ranking algorithms was the average prediction probability concordance (PK) using scores from two pathologists as the TC reference standard.  ...  The mention of commercial products, their sources, or their use in connection with material reported herein is not to be construed as either an actual or implied endorsement of such products by the Department  ... 
doi:10.1117/1.jmi.8.3.034501 pmid:33987451 pmcid:PMC8107263 fatcat:2fzlahbdjjewpdy5r7z7sqfeuy

Self supervised contrastive learning for digital histopathology [article]

Ozan Ciga, Tony Xu, Anne L. Martel
2021 arXiv   pre-print
In this paper, we use a contrastive self-supervised learning method called SimCLR that achieved state-of-the-art results on natural-scene images and apply this method to digital histopathology by collecting  ...  Furthermore, we find using more images for pretraining leads to a better performance in multiple downstream tasks.  ...  Assessment of residual breast cancer cellularity after neoadjuvant chemotherapy using digital pathology [data set].  ... 
arXiv:2011.13971v2 fatcat:a53vlhvdvzdhlncv2gir4tttf4

Front Matter: Volume 10581

Metin N. Gurcan, John E. Tomaszewski
2018 Medical Imaging 2018: Digital Pathology  
SPIE uses a seven-digit CID article numbering system structured as follows:  The first five digits correspond to the SPIE volume number.  The last two digits indicate publication order within the volume  ...  Publication of record for individual papers is online in the SPIE Digital Library. Paper Numbering: Proceedings of SPIE follow an e-First publication model.  ...  We would like to acknowledge the excellent work in the following papers:  ... 
doi:10.1117/12.2323941 fatcat:wyt7wxgl4nebxooizvt2efswwq

H&E-stained Whole Slide Image Deep Learning Predicts SPOP Mutation State in Prostate Cancer [article]

Andrew J Schaumberg, Mark A Rubin, Thomas J Fuchs
2016 bioRxiv   pre-print
To our knowledge, this is the first statistical model to predict a genetic mutation in cancer directly from the patient's digitized H&E-stained whole microscopy slide.  ...  We constructed a statistical model that predicts whether or not SPOP is mutated in prostate cancer, given only the digital whole slide after standard hematoxylin and eosin [H&E] staining.  ...  the patient slide dominant tumor morphology.  ... 
doi:10.1101/064279 fatcat:cny6vqnbdrgfjnbcw6pu6zw7ze

Translational AI and Deep Learning in Diagnostic Pathology

Ahmed Serag, Adrian Ion-Margineanu, Hammad Qureshi, Ryan McMillan, Marie-Judith Saint Martin, Jim Diamond, Paul O'Reilly, Peter Hamilton
2019 Frontiers in Medicine  
The clearance of digital pathology for primary diagnosis in the US by some manufacturers provides the platform on which to deliver practical AI.  ...  This is resulting in the innovation of deep learning technologies that are specifically aimed at cellular imaging and practical applications that could transform diagnostic pathology.  ...  digital slides to pathology staff inside and outside an organization, manually reviewing digital slides on-screen rather than using a microscope and reporting cases in an entirely digital workspace.  ... 
doi:10.3389/fmed.2019.00185 pmid:31632973 pmcid:PMC6779702 fatcat:jd5arv2lc5aq3lbk7ztipt5mca

Multi-scale Deep Learning Architecture for Nucleus Detection in Renal Cell Carcinoma Microscopy Image [article]

Shiba Kuanar, Vassilis Athitsos, Dwarikanath Mahapatra, Anand Rajan
2021 arXiv   pre-print
Therefore, better cell nucleus detection and counting techniques can be an important biomarker for the assessment of tumor cell proliferation in routine pathological investigations.  ...  Manual counting of tumor cells in the tissue-affected sections is one of the strongest prognostic markers for renal cancer. However, this procedure is time-consuming and also prone to subjectivity.  ...  Each whole scan slide includes thousands of cells and small cellular structures.  ... 
arXiv:2104.13557v1 fatcat:wkdjgkvj4jf7do4vuyb4xi3vqy

Automatic and explainable grading of meningiomas from histopathology images [article]

Jonathan Ganz, Tobias Kirsch, Lucas Hoffmann, Christof A. Bertram, Christoph Hoffmann, Andreas Maier, Katharina Breininger, Ingmar Blümcke, Samir Jabari, Marc Aubreville
2021 arXiv   pre-print
In the second stage, we calculate a score corresponding to tumor malignancy based on information contained in this region using three different settings.  ...  Meningioma is one of the most prevalent brain tumors in adults. To determine its malignancy, it is graded by a pathologist into three grades according to WHO standards.  ...  In contrast to the WHO grade, in this study we used a continuous score to determine tumor malignancy.  ... 
arXiv:2107.08850v1 fatcat:y3bazm636zfbfidjjivlet3bmq

Abstracts from USCAP 2021: Informatics (771-794)

2021 Modern Pathology  
Evaluation of tissues by a pathologist to assess tumor cellularity, inflammation, fibrosis, and necrosis, is essential for specimen-centered clinical trials.  ...  The pathologists were required to review glass slides on location only in those few cases where digital slides were not available.  ...  3)Pathologists estimated tumor cellularity (Path-Score), the percentage of tumor cells in total cells. 4)Pathologists reviewed the results of AI analysis (AI-Score). 5)The pathologists determined the  ... 
doi:10.1038/s41379-021-00765-2 pmid:33707680 fatcat:zighvw57hrhg7disjtecaeefee

Abstracts from USCAP 2021: Informatics (771-794)

2021 Laboratory Investigation  
Evaluation of tissues by a pathologist to assess tumor cellularity, inflammation, fibrosis, and necrosis, is essential for specimen-centered clinical trials.  ...  The pathologists were required to review glass slides on location only in those few cases where digital slides were not available.  ...  3)Pathologists estimated tumor cellularity (Path-Score), the percentage of tumor cells in total cells. 4)Pathologists reviewed the results of AI analysis (AI-Score). 5)The pathologists determined the  ... 
doi:10.1038/s41374-021-00563-z pmid:33707725 fatcat:2r5ijt565zdypdjkkvx3lxowfi

Histopathological Classification of Canine Cutaneous Round Cell Tumors Using Deep Learning: A Multi-Center Study

Massimo Salvi, Filippo Molinari, Selina Iussich, Luisa Vera Muscatello, Luca Pazzini, Silvia Benali, Barbara Banco, Francesca Abramo, Raffaella De Maria, Luca Aresu
2021 Frontiers in Veterinary Science  
For mast cell tumors the reduction of a grade was observed in the train set, but not in the test set.  ...  In this study we describe ARCTA (Automated Round Cell Tumors Assessment), a fully automated algorithm for cutaneous RCT classification and mast cell tumors grading in canine histopathological images.  ...  Digital images were scanned with a magnification of x400 (conversion factor: 0.233 µm/pixel) using a Hamamatsu NanoZoomer S210 Digital slide scanner.  ... 
doi:10.3389/fvets.2021.640944 pmid:33869320 pmcid:PMC8044886 fatcat:dghueoqmlbfmrdkqzexqxh5434

PathoFusion: An Open-Source AI Framework for Recognition of Pathomorphological Features and Mapping of Immunohistochemical Data

Guoqing Bao, Xiuying Wang, Ran Xu, Christina Loh, Oreoluwa Daniel Adeyinka, Dula Asheka Pieris, Svetlana Cherepanoff, Gary Gracie, Maggie Lee, Kerrie L. McDonald, Anna K. Nowak, Richard Banati (+2 others)
2021 Cancers  
Image tiles cropped from the digitized images based on markings made by a consultant neuropathologist were used to train the BCNN.  ...  This is analogous to how a microscopist in pathology works, identifying a cancerous morphological feature in the tissue context using first a narrow and then a wider focus, hence bifocal.  ...  The histological slides used in pathology and neuropathology can be scanned and converted into digital images.  ... 
doi:10.3390/cancers13040617 pmid:33557152 pmcid:PMC7913958 fatcat:gmtvxokw6ndi3ad3syeizhpg2q
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