# Photometric Transformer Networks and Label Adjustment for Breast Density Prediction

arXiv: https://arxiv.org/abs/1905.02906 · HTML: https://mlsanity.com/paper/1905.02906/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2019.

## Built on by (most cited first, 10 of 10)

- [Breaking the Dilemma of Medical Image-to-image Translation](https://mlsanity.com/paper/2110.06465/) (2021)
- [LongReMix: Robust Learning with High Confidence Samples in a Noisy Label Environment](https://mlsanity.com/paper/2103.04173/) (2021)
- [PropMix: Hard Sample Filtering and Proportional MixUp for Learning with Noisy Labels](https://mlsanity.com/paper/2110.11809/) (2021)
- [ScanMix: Learning from Severe Label Noise via Semantic Clustering and Semi-Supervised Learning](https://mlsanity.com/paper/2103.11395/) (2021)
- [Learning to Complement and to Defer to Multiple Users](https://mlsanity.com/paper/2407.07003/) (2024)
- [Learning to Complement with Multiple Humans](https://mlsanity.com/paper/2311.13172/) (2023)
- [A Study on the Impact of Data Augmentation for Training Convolutional Neural Networks in the Presence of Noisy Labels](https://mlsanity.com/paper/2208.11176/) (2022)
- [CNTN: Cyclic Noise-tolerant Network for Gait Recognition](https://mlsanity.com/paper/2210.06910/) (2022)
- [Maximising the Utility of Validation Sets for Imbalanced Noisy-label Meta-learning](https://mlsanity.com/paper/2208.08132/) (2022)
- [Elucidating Meta-Structures of Noisy Labels in Semantic Segmentation by Deep Neural Networks](https://mlsanity.com/paper/2205.00160/) (2022)

## Abstract (arXiv)

Grading breast density is highly sensitive to normalization settings of digital mammogram as the density is tightly correlated with the distribution of pixel intensity. Also, the grade varies with readers due to uncertain grading criteria. These issues are inherent in the density assessment of digital mammography. They are problematic when designing a computer-aided prediction model for breast density and become worse if the data comes from multiple sites. In this paper, we proposed two novel deep learning techniques for breast density prediction: 1) photometric transformation which adaptively normalizes the input mammograms, and 2) label distillation which adjusts the label by using its output prediction. The photometric transformer network predicts optimal parameters for photometric transformation on the fly, learned jointly with the main prediction network. The label distillation, a type of pseudo-label techniques, is intended to mitigate the grading variation. We experimentally showed that the proposed methods are beneficial in terms of breast density prediction, resulting in significant performance improvement compared to various previous approaches.