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New deep learning challenge to estimate breast
density from mammograms
A new deep learning challenge has been launched by the UEF Cancer
AI research team at the University of Eastern Finland, led by senior
researcher Hamid Behravan, PhD, and funded by Sitra, the Finnish
Innovation Fund.
The challenge aims to develop an architecture capable of
automatically estimating the breast percentage density from
mammograms. High breast tissue density is a signi昀椀cant risk factor for
breast cancer.
Breast density refers to the proportion of dense tissue to fatty tissue
in the breast. Women with dense breasts have a higher risk of breast
cancer than women with fatty breasts. Women with very dense
breasts are 4-5 times more likely to get breast cancer than women
with fatty breasts.
Current computer-aided design tools for estimating breast density
percentages in mammograms often have limitations, such as being
restricted to speci昀椀c mammogram views, struggling with complete
delineation of the pectoral muscle, and performing poorly in cases of
data variability. These tools also require an experienced radiologist to
adjust the segmentation threshold for dense tissue within the breast
area.
The challenge calls for the development of a new deep learning
architecture that can overcome these limitations and automatically
estimate the area-based breast percentage density from
mammograms. The challenge welcomes a range of approaches,
including both regression and segmentation methods.
Participants will be training their models on a dataset of 569
mammogram images and testing their performance on a separate
set of 149 images. A source code for a baseline segmentation
approach is available in the provided Github repository. Participants
are encouraged to utilize and enhance this model for the challenge
density estimation task.
Graphical abstract of an advanced architecture for accurate
mammogram segmentation
By participating in this challenge, participants will be contributing to
a solution that could potentially lead to earlier detection of breast
cancer and prevention. They will also be applying their deep learning
and image analysis skills to a real-world problem with signi昀椀cant
public health implications.
The challenge is open for submissions until March 31, 2024. The
winners will be announced on April 1, 2024. We extend an invitation
to the top three teams on the Leaderboard to collaborate with us in
writing a manuscript.
For more information and to register for the challenge, please visit
the challenge website:
https://www.kaggle.com/competitions/breast-density-prediction.
Senior researcher Hamid Behravan,
https://uefconnect.uef.昀椀/en/person/hamid.behravan/
More information at https://github.com/uefcancer/Deepdensity
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Issue 399
December
2023
9