Screening Mammography Breast Cancer Detection

07/21/2023
by   Debajyoti Chakraborty, et al.
0

Breast cancer is a leading cause of cancer-related deaths, but current programs are expensive and prone to false positives, leading to unnecessary follow-up and patient anxiety. This paper proposes a solution to automated breast cancer detection, to improve the efficiency and accuracy of screening programs. Different methodologies were tested against the RSNA dataset of radiographic breast images of roughly 20,000 female patients and yielded an average validation case pF1 score of 0.56 across methods.

READ FULL TEXT
research
04/13/2022

A deep learning algorithm for reducing false positives in screening mammography

Screening mammography improves breast cancer outcomes by enabling early ...
research
04/28/2020

Deep Learning-based Mammogram Classification using Small Dataset

Breast Cancer is one of the most diagnosed cancer and the leading cause ...
research
05/14/2018

An Automatic Patch-based Approach for HER-2 Scoring in Immunohistochemical Breast Cancer Images Using Color Features

Breast cancer (BC) is the most common cancer among women world-wide, app...
research
12/23/2019

Robust breast cancer detection in mammography and digital breast tomosynthesis using annotation-efficient deep learning approach

Breast cancer remains a global challenge, causing over 1 million deaths ...
research
06/10/2022

A multi-objective constrained POMDP model for breast cancer screening

Breast cancer is a common and deadly disease, but it is often curable wh...
research
10/31/2020

Segmentation of Infrared Breast Images Using MultiResUnet Neural Network

Breast cancer is the second leading cause of death for women in the U.S....

Please sign up or login with your details

Forgot password? Click here to reset