Isfahan University of Medical Sciences

Science Communicator Platform

Stay connected! Follow us on X network (Twitter):
Share this content! On (X network) By
A Histopathological Image Dataset for Grading Breast Invasive Ductal Carcinomas Publisher



Bolhasani H1 ; Amjadi E2 ; Tabatabaeian M3 ; Jassbi SJ1
Authors
Show Affiliations
Authors Affiliations
  1. 1. Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
  2. 2. Poursina Hakim Digestive Diseases Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
  3. 3. Anahid Community-Based Breast Clinic, Isfahan, Iran

Source: Informatics in Medicine Unlocked Published:2020


Abstract

Breast cancer is a common cancer in women, and one of the major causes of death among women around the world. Invasive ductal carcinoma (IDC) is the most widespread type of breast cancer with about 80% of all diagnosed cases. Early accurate diagnosis plays an important role in choosing the right treatment plan and improving survival rate among the patients. In recent years, efforts have been made to predict and detect all types of cancers by employing artificial intelligence. An appropriate dataset is the first essential step to achieve such a goal. This paper introduces a histopathological microscopy image dataset of 922 images related to 124 patients with IDC. The dataset has been published and is accessible through the web at: http://databiox.com. The distinctive feature of this dataset as compared to similar ones is that it contains an equal number of specimens from each of three grades of IDC, which leads to approximately 50 specimens for each grade. © 2020 The Authors
Related Docs
View other Related Docs
1. Unsupervised Fuzzy Cognitive Map in Diagnosis of Breast Epithelial Lesions, 2015 22nd Iranian Conference on Biomedical Engineering, ICBME 2015 (2016)
2. Differentiation Between Pancreatic Ductal Adenocarcinoma and Normal Pancreatic Tissue for Treatment Response Assessment Using Multi-Scale Texture Analysis of Ct Images, Journal of Biomedical Physics and Engineering (2022)
3. Ensemble Supervised Classification Method Using the Regions of Interest and Grey Level Co-Occurrence Matrices Features for Mammograms Data, Iranian Journal of Radiology (2015)
4. Learning Melanocytic Proliferation Segmentation in Histopathology Images From Imperfect Annotations, IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (2021)
5. A Hybrid Computer-Aided-Diagnosis System for Prediction of Breast Cancer Recurrence (Hpbcr) Using Optimized Ensemble Learning, Computational and Structural Biotechnology Journal (2017)
6. Identification of Effective Factors in Breast Cancer Survival in Isfahan Using Machine Learning Techniques, Journal of Advances in Medical and Biomedical Research (2024)
7. Classification and Staging of Brain Glioma Tumors Using Magnetic Resonance Imaging and Machine Learning Algorithms, Journal of Isfahan Medical School (2022)
8. Breast Cancer Diagnosis in Dce-Mri Using Mixture Ensemble of Convolutional Neural Networks, Pattern Recognition (2017)
Experts (# of related papers)
View all Related Experts
Alireza Mehri-Dehnavi (2)
Alireza Vard (2)
Other Related Docs
9. Computer Aided Measurement of Melanoma Depth of Invasion in Microscopic Images, Micron (2014)
10. Diagnostic Performance Evaluation of Multiparametric Magnetic Resonance Imaging in the Detection of Prostate Cancer With Supervised Machine Learning Methods, Diagnostics (2023)
11. Plasmacell Cad: A Computer-Aided Diagnosis Software Tool for Plasma Cell Recognition and Characterization in Microscopic Images, International Journal of Medical Informatics (2025)
12. Diagnosis of Cervical Cancer Using Texture and Morphological Features in Pap Smear Images, Journal of Isfahan Medical School (2020)