# Interpretable Automatic Rosacea Detection with Whitened Cosine Similarity

arXiv: https://arxiv.org/abs/2504.08073 · HTML: https://mlsanity.com/paper/2504.08073/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 20 references that are in the map.
- First posted on arXiv 10 Apr 2025.

## Builds on (most cited first, 4 of 4)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [Increasing Rosacea Awareness Among Population Using Deep Learning and Statistical Approaches](https://mlsanity.com/paper/2411.07074/) (2024)

## Built on by (most cited first, 3 of 3)

- [Laparoscopic Image Desmoking Using the U-Net with New Loss Function and Integrated Differentiable Wiener Filter](https://mlsanity.com/paper/2505.21634/) (2025)
- [Patch-based Automatic Rosacea Detection Using the ResNet Deep Learning Framework](https://mlsanity.com/paper/2509.09841/) (2025)
- [Privacy-Preserving Automated Rosacea Detection Based on Medically Inspired Region of Interest Selection](https://mlsanity.com/paper/2509.09844/) (2025)

## Abstract (arXiv)

According to the National Rosacea Society, approximately sixteen million Americans suffer from rosacea, a common skin condition that causes flushing or long-term redness on a person's face. To increase rosacea awareness and to better assist physicians to make diagnosis on this disease, we propose an interpretable automatic rosacea detection method based on whitened cosine similarity in this paper. The contributions of the proposed methods are three-fold. First, the proposed method can automatically distinguish patients suffering from rosacea from people who are clean of this disease with a significantly higher accuracy than other methods in unseen test data, including both classical deep learning and statistical methods. Second, the proposed method addresses the interpretability issue by measuring the similarity between the test sample and the means of two classes, namely the rosacea class versus the normal class, which allows both medical professionals and patients to understand and trust the results. And finally, the proposed methods will not only help increase awareness of rosacea in the general population, but will also help remind patients who suffer from this disease of possible early treatment, as rosacea is more treatable in its early stages. The code and data are available at https://github.com/chengyuyang-njit/ICCRD-2025. The code and data are available at https://github.com/chengyuyang-njit/ICCRD-2025.