A framework for distinguishing benign from malignant breast histopathological images using deep residual networks

Ziba Gandomkar; Patrick C. Brennan; Claudia Mello-Thoms

doi:10.1117/12.2318320

6 July 2018 A framework for distinguishing benign from malignant breast histopathological images using deep residual networks

Ziba Gandomkar, Patrick C. Brennan, Claudia Mello-Thoms

Author Affiliations +

Proceedings Volume 10718, 14th International Workshop on Breast Imaging (IWBI 2018); 107180U (2018) https://doi.org/10.1117/12.2318320
Event: The Fourteenth International Workshop on Breast Imaging, 2018, Atlanta, Georgia, United States

Abstract

Studies have shown that there are discrepancies among pathologists in the classification of breast histopathological slides. In this study we propose a framework for categorizing hematoxylin-eosin stained breast images either as benign or malignant at four magnification factors, and then aggregating the classification results of a patient’s images from different magnification factors to make the ultimate diagnosis for each patient. We used a publicly available database, containing 7786 images from 81 patients. The images were acquired in four visual magnification factors, namely x40, x100, x200, and x400, with an effective pixel size of 0.49 μm, 0.20 μm, 0.10 μm, and 0.05 μm respectively. In order to mitigate the inconsistencies in the color of the images, stain normalization was performed. Next, for each magnification factor, a deep residual network (ResNet) with 152 layers has been trained for classifying patches from the images as benign or malignant. Then, a meta-decision tree was used to combine classification results of a patient’s images from different magnification factors to provide a patient-level diagnosis. The ResNets achieved correct classification rates (CCR) of 98.52%, 97.90%, 98.33%, and 97.66% at x40, x100, x200, and x400 magnification factors, respectively. For classification of patients either as benign or malignant, a CCR of 98.77% was obtained. In conclusion, our study showed that the proposed framework can be helpful in the categorization of breast digital slides.

Citation Download Citation

Ziba Gandomkar, Patrick C. Brennan, and Claudia Mello-Thoms "A framework for distinguishing benign from malignant breast histopathological images using deep residual networks", Proc. SPIE 10718, 14th International Workshop on Breast Imaging (IWBI 2018), 107180U (6 July 2018); https://doi.org/10.1117/12.2318320

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available