Privacy-Preserving Automated Rosacea Detection Based on Medically Inspired Region of Interest Selection

• URL: http://arxiv.org/abs/2509.09844v1
• Date: Thu, 11 Sep 2025 20:54:26 GMT
• Title: Privacy-Preserving Automated Rosacea Detection Based on Medically Inspired Region of Interest Selection
• Authors: Chengyu Yang, Rishik Reddy Yesgari, Chengjun Liu,
• Abstract summary: Rosacea is a common but underdiagnosed inflammatory skin condition that primarily affects the central face.<n>A novel privacy-preserving automated rosacea detection method inspired by clinical priors and trained entirely on synthetic data is presented.
• Score: 4.561078525225695
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Rosacea is a common but underdiagnosed inflammatory skin condition that primarily affects the central face and presents with subtle redness, pustules, and visible blood vessels. Automated detection remains challenging due to the diffuse nature of symptoms, the scarcity of labeled datasets, and privacy concerns associated with using identifiable facial images. A novel privacy-preserving automated rosacea detection method inspired by clinical priors and trained entirely on synthetic data is presented in this paper. Specifically, the proposed method, which leverages the observation that rosacea manifests predominantly through central facial erythema, first constructs a fixed redness-informed mask by selecting regions with consistently high red channel intensity across facial images. The mask thus is able to focus on diagnostically relevant areas such as the cheeks, nose, and forehead and exclude identity-revealing features. Second, the ResNet-18 deep learning method, which is trained on the masked synthetic images, achieves superior performance over the full-face baselines with notable gains in terms of accuracy, recall and F1 score when evaluated using the real-world test data. The experimental results demonstrate that the synthetic data and clinical priors can jointly enable accurate and ethical dermatological AI systems, especially for privacy sensitive applications in telemedicine and large-scale screening.

Related papers

• Patch-based Automatic Rosacea Detection Using the ResNet Deep Learning Framework [4.561078525225695]
  Rosacea is a chronic inflammatory skin condition that manifests with facial redness, papules, and visible blood vessels.<n>This paper presents new patch-based automatic rosacea detection strategies using the ResNet-18 deep learning framework.
  arXiv  Detail & Related papers  (2025-09-11T20:44:13Z)
• Zero-shot Segmentation of Skin Conditions: Erythema with Edit-Friendly Inversion [0.27624021966289597]
  This study proposes a zero-shot image segmentation framework for detecting erythema (redness of the skin) using edit-friendly inversion in diffusion models.<n>The method synthesizes reference images of the same patient that are free from erythema via generative editing and then accurately aligns these references with the original images.
  arXiv  Detail & Related papers  (2025-08-02T12:00:35Z)
• FairSkin: Fair Diffusion for Skin Disease Image Generation [54.29840149709033]
  Diffusion Model (DM) has become a leading method in generating synthetic medical images, but it suffers from a critical twofold bias. We propose FairSkin, a novel DM framework that mitigates these biases through a three-level resampling mechanism. Our approach significantly improves the diversity and quality of generated images, contributing to more equitable skin disease detection in clinical settings.
  arXiv  Detail & Related papers  (2024-10-29T21:37:03Z)
• CFCPalsy: Facial Image Synthesis with Cross-Fusion Cycle Diffusion Model for Facial Paralysis Individuals [6.9859169233703104]
  This study aims to synthesize a high-quality facial paralysis dataset to address this gap.<n>A novel Cross-Fusion Cycle Palsy Expression Generative Model (PalsyCFC) based on the diffusion model is proposed.<n>We have qualitatively and quantitatively evaluated the proposed method on the commonly used public clinical datasets of facial paralysis.
  arXiv  Detail & Related papers  (2024-09-11T13:46:35Z)
• OpticalDR: A Deep Optical Imaging Model for Privacy-Protective Depression Recognition [66.91236298878383]
  Depression Recognition (DR) poses a considerable challenge, especially in the context of privacy concerns. We design a new imaging system to erase the identity information of captured facial images while retain disease-relevant features. It is irreversible for identity information recovery while preserving essential disease-related characteristics necessary for accurate DR.
  arXiv  Detail & Related papers  (2024-02-29T01:20:29Z)
• Optimizing Skin Lesion Classification via Multimodal Data and Auxiliary Task Integration [54.76511683427566]
  This research introduces a novel multimodal method for classifying skin lesions, integrating smartphone-captured images with essential clinical and demographic information. A distinctive aspect of this method is the integration of an auxiliary task focused on super-resolution image prediction. The experimental evaluations have been conducted using the PAD-UFES20 dataset, applying various deep-learning architectures.
  arXiv  Detail & Related papers  (2024-02-16T05:16:20Z)
• High Fidelity Synthetic Face Generation for Rosacea Skin Condition from Limited Data [0.27998963147546135]
  StyleGANs, mainly variants of StyleGANs, have demonstrated promising results in generating synthetic facial images. A small dataset of Rosacea with 300 full-face images is utilized to further investigate the possibility of generating synthetic data. The preliminary experiments show how fine-tuning the model and varying experimental settings significantly affect the fidelity of the Rosacea features.
  arXiv  Detail & Related papers  (2023-03-08T19:18:58Z)
• Assessing glaucoma in retinal fundus photographs using Deep Feature Consistent Variational Autoencoders [63.391402501241195]
  glaucoma is challenging to detect since it remains asymptomatic until the symptoms are severe. Early identification of glaucoma is generally made based on functional, structural, and clinical assessments. Deep learning methods have partially solved this dilemma by bypassing the marker identification stage and analyzing high-level information directly to classify the data.
  arXiv  Detail & Related papers  (2021-10-04T16:06:49Z)
• Facial Anatomical Landmark Detection using Regularized Transfer Learning with Application to Fetal Alcohol Syndrome Recognition [24.27777060287004]
  Fetal alcohol syndrome (FAS) caused by prenatal alcohol exposure can result in a series of cranio-facial anomalies. Anatomical landmark detection is important to detect the presence of FAS associated facial anomalies. Current deep learning-based heatmap regression methods designed for facial landmark detection in natural images assume availability of large datasets. We develop a new regularized transfer learning approach that exploits the knowledge of a network learned on large facial recognition datasets.
  arXiv  Detail & Related papers  (2021-09-12T11:05:06Z)
• End2End Occluded Face Recognition by Masking Corrupted Features [82.27588990277192]
  State-of-the-art general face recognition models do not generalize well to occluded face images. This paper presents a novel face recognition method that is robust to occlusions based on a single end-to-end deep neural network. Our approach, named FROM (Face Recognition with Occlusion Masks), learns to discover the corrupted features from the deep convolutional neural networks, and clean them by the dynamically learned masks.
  arXiv  Detail & Related papers  (2021-08-21T09:08:41Z)
• Retinopathy of Prematurity Stage Diagnosis Using Object Segmentation and Convolutional Neural Networks [68.96150598294072]
  Retinopathy of Prematurity (ROP) is an eye disorder primarily affecting premature infants with lower weights. It causes proliferation of vessels in the retina and could result in vision loss and, eventually, retinal detachment, leading to blindness. In recent years, there has been a significant effort to automate the diagnosis using deep learning. This paper builds upon the success of previous models and develops a novel architecture, which combines object segmentation and convolutional neural networks (CNN) Our proposed system first trains an object segmentation model to identify the demarcation line at a pixel level and adds the resulting mask as an additional "color" channel in
  arXiv  Detail & Related papers  (2020-04-03T14:07:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.