Simple non-iterative clustering and CNNs for coarse segmentation of breast cancer whole-slide images

2021 ◽  
Author(s):  
Rawan Albusayli ◽  
Dinny Graham ◽  
Nirmala Pathmanathan ◽  
Muhammad Shaban ◽  
Fayyaz Minhas ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Whole Slide Images

Validity of whole slide images for scoring HER2 chromogenic in situ hybridisation in breast cancer

2016 ◽  
Vol 69 (11) ◽  
pp. 992-997 ◽  
Author(s):  
Shaimaa Al-Janabi ◽  
Anja Horstman ◽  
Henk-Jan van Slooten ◽  
Chantal Kuijpers ◽  
Clifton Lai-A-Fat ◽  
...  
Keyword(s):  
Breast Cancer ◽  
In Situ Hybridisation ◽  
Whole Slide Images

scholarly journals Simultaneous automatic scoring and co-registration of hormone receptors in tumor areas in whole slide images of breast cancer tissue slides

2016 ◽  
Vol 91 (6) ◽  
pp. 585-594 ◽  
Author(s):  
Nicholas Trahearn ◽  
Yee Wah Tsang ◽  
Ian A. Cree ◽  
David Snead ◽  
David Epstein ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Hormone Receptors ◽  
Breast Cancer Tissue ◽  
Cancer Tissue ◽  
Automatic Scoring ◽  
Whole Slide Images

scholarly journals Breast Tumor Cellularity Assessment using Deep Neural Networks

2019 ◽  
Author(s):  
Alexander Rakhlin ◽  
Aleksei Tiulpin ◽  
Alexey A. Shvets ◽  
Alexandr A. Kalinin ◽  
Vladimir I. Iglovikov ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Automatic Assessment ◽  
Appropriate Treatment ◽  
Image Patches ◽  
Expert Pathologist ◽  
Hematoxylin And Eosin ◽  
Treated Breast ◽  
Intra Class Correlation Coefficient ◽  
Whole Slide Images

AbstractBreast cancer is one of the main causes of death world-wide. Histopathological cellularity assessment of residual tumors in post-surgical tissues is used to analyze a tumor’s response to a therapy. Correct cellularity assessment increases the chances of getting an appropriate treatment and facilitates the patient’s survival. In current clinical practice, tumor cellularity is manually estimated by pathologists; this process is tedious and prone to errors or low agreement rates between assessors. In this work, we evaluated three strong novel Deep Learning-based approaches for automatic assessment of tumor cellularity from post-treated breast surgical specimens stained with hematoxylin and eosin. We validated the proposed methods on the BreastPathQ SPIE challenge dataset that consisted of 2395 image patches selected from whole slide images acquired from 64 patients. Compared to expert pathologist scoring, our best performing method yielded the Cohen’s kappa coefficient of 0.69 (vs. 0.42 previously known in literature) and the intra-class correlation coefficient of 0.89 (vs. 0.83). Our results suggest that Deep Learning-based methods have a significant potential to alleviate the burden on pathologists, enhance the diagnostic workflow, and, thereby, facilitate better clinical outcomes in breast cancer treatment.

scholarly journals Genetic mutation and biological pathway prediction based on whole slide images in breast carcinoma using deep learning

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Hui Qu ◽  
Mu Zhou ◽  
Zhennan Yan ◽  
He Wang ◽  
Vinod K. Rustgi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Breast Carcinoma ◽  
Targeted Therapies ◽  
Metastatic Breast ◽  
Image Features ◽  
Genetic Mutations ◽  
Biological Pathway ◽  
Visual Interactions ◽  
Whole Slide Images

AbstractBreast carcinoma is the most common cancer among women worldwide that consists of a heterogeneous group of subtype diseases. The whole-slide images (WSIs) can capture the cell-level heterogeneity, and are routinely used for cancer diagnosis by pathologists. However, key driver genetic mutations related to targeted therapies are identified by genomic analysis like high-throughput molecular profiling. In this study, we develop a deep-learning model to predict the genetic mutations and biological pathway activities directly from WSIs. Our study offers unique insights into WSI visual interactions between mutation and its related pathway, enabling a head-to-head comparison to reinforce our major findings. Using the histopathology images from the Genomic Data Commons Database, our model can predict the point mutations of six important genes (AUC 0.68–0.85) and copy number alteration of another six genes (AUC 0.69–0.79). Additionally, the trained models can predict the activities of three out of ten canonical pathways (AUC 0.65–0.79). Next, we visualized the weight maps of tumor tiles in WSI to understand the decision-making process of deep-learning models via a self-attention mechanism. We further validated our models on liver and lung cancers that are related to metastatic breast cancer. Our results provide insights into the association between pathological image features, molecular outcomes, and targeted therapies for breast cancer patients.

scholarly journals COMPARATIVE FEATURES OF OPEN SOURCE SOFTWARE PRODUCTS FOR THE DEVELOPMENT OF AN AUTOMATED BREAST CANCER DIAGNOSTIC PROGRAM

2019 ◽  
Vol 7 (4) ◽  
pp. 377-385
Author(s):  
V. Kovalev ◽  
Y. Diachenko ◽  
V. Malyshev ◽  
S. Rjabceva ◽  
O. Kolomiets ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Open Source ◽  
Cancer Diagnosis ◽  
Open Source Software ◽  
Imaging Techniques ◽  
Digital Pathology ◽  
Third Party ◽  
Histopathological Diagnosis ◽  
Histological Verification ◽  
Whole Slide Images

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 techniques to assist pathologists in quantitative and qualitative evaluation of histopathological preparations. The development of software for morphological diagnosis is important for improving the quality of histological verification of diagnosis in oncopathology. The purpose of this work is to find and benchmark existing open-source software for the whole-slide histological images processing. Choosing an open source program is an important step in developing an automated breast cancer diagnosis program. The result is a detailed study of open-source software: ASAP, Orbit, Cytomine and QuPath. Their features and methods of image processing were analyzed. QuPath software has the best characteristics for extending it with an automated module for the cancer diagnosis. QuPath combines a user-friendly, easy-to-use interface, customizable functionality, and moderate computing power requirements. Besides, QuPath works with whole-slide images with immunohistochemical markers; features implemented in this software allow making a morphometric analysis. QuPath saves time for a graphical user interface development and provides a scalable system to add new key features. QuPath supports third-party MATLAB and Python extensions.

scholarly journals Visual assessment of mitotic figures in breast cancer: a comparative study between light microscopy and whole slide images

2021 ◽  
Author(s):  
Ayat Lashen ◽  
Asmaa Ibrahim ◽  
Ayaka Katayama ◽  
Graham Ball ◽  
Raluca Mihai ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Comparative Study ◽  
Light Microscopy ◽  
Visual Assessment ◽  
Mitotic Figures ◽  
Whole Slide Images
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