# A closer look onto breast density with weakly supervised dense-tissue masks

## TL;DR

This paper introduces a weakly supervised method for automatically quantifying breast density from digital mammograms, predicting continuous density percentages and dense tissue regions using only categorical labels.

## Contribution

It proposes a novel weakly supervised loss function that links density percentage to pixel-wise dense tissue masks, enabling detailed density estimation with minimal supervision.

## Key findings

- Accurately predicts continuous breast density percentages.
- Provides pixel-wise dense tissue support maps.
- Achieves effective density quantification with weak supervision.

## Abstract

This work focuses on the automatic quantification of the breast density from digital mammography imaging. Using only categorical image-wise labels we train a model capable of predicting continuous density percentage as well as providing a pixel wise support frit for the dense region. In particular we propose a weakly supervised loss linking the density percentage to the mask size.