Objects characterization-based approach to enhance detection of degree of malignancy in breast cancer histopathology

Pedro Furtado

doi:10.1117/12.2511986

15 March 2019 Objects characterization-based approach to enhance detection of degree of malignancy in breast cancer histopathology

Pedro Furtado

Author Affiliations +

Proceedings Volume 10949, Medical Imaging 2019: Image Processing; 109491R (2019) https://doi.org/10.1117/12.2511986
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

Histologic grading from images has become widely accepted as a powerful indicator of prognosis in breast cancer. Automated grading can assist the doctor diagnosing the medical condition. But algorithms still lag behind human experts in this task, as human experts excel in identifying parts, detecting characteristics and relating concepts and semantics. This can be improved by making algorithms distinguish and characterize the most relevant types of objects in the image and characterizing images from that. We propose a three-stage automated approach named OBI (Object-based Identification) with steps: 1. Object-based identification, which identifies the “type of object” of each region and characterizes it; 2. Learn about image, which characterizes distribution of characteristics of those types of objects in image; 3. Determination of degree of malignancy, which assigns a degree of malignancy based on a classifier over object type characteristics (the statistical distribution of characteristics of structures) in the image. Our proof-of-concept prototype uses publicly-available Mytos-Atypia dataset [19] to compare accuracy with alternatives. Results summary: human expert (medical doctor) 84%, classic machine learning 74%, convolution neural networks (CNN), 78%, our approach (OBI) 86%. As future work, we expect to generalize our results to other datasets and problems, explore mimicking knowledge of human concepts further, merge the object-based approach with CNN techniques and adapt it to other medical imaging contexts.

Conference Presentation

Citation Download Citation

Pedro Furtado "Objects characterization-based approach to enhance detection of degree of malignancy in breast cancer histopathology", Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 109491R (15 March 2019); https://doi.org/10.1117/12.2511986

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