SkinScan: Low-Cost 3D-Scanning for Dermatologic Diagnosis and Documentation
The utilization of computational photography becomes increasingly essential in the medical field. Today, imaging techniques for dermatology range from two-dimensional (2D) color imagery with a mobile device to professional clinical imaging systems measuring additional detailed three-dimensional (3D) data. The latter are commonly expensive and not accessible to a broad audience. In this work, we propose a novel system and software framework that relies only on low-cost (and even mobile) commodity devices present in every household to measure detailed 3D information of the human skin with a 3D-gradient-illumination-based method. We believe that our system has great potential for early-stage diagnosis and monitoring of skin diseases, especially in vastly populated or underdeveloped areas.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
FairDD: Enhancing Fairness with domain-incremental learning in dermatological disease diagnosis
With the rapid advancement of deep learning technologies, artificial intelligence has become increasingly prevalent in the research and application of dermatological disease diagnosis. However, this data-driven approach …
Contrastive LearningData AugmentationDiagnosticFairness+1Federated Contrastive Learning for Dermatological Disease Diagnosis via On-device Learning
Deep learning models have been deployed in an increasing number of edge and mobile devices to provide healthcare. These models rely on training with a tremendous amount of labeled data to achieve high accuracy. However, …
Contrastive LearningFederated LearningSelf-Supervised LearningUMass-BioNLP at MEDIQA-M3G 2024: DermPrompt -- A Systematic Exploration of Prompt Engineering with GPT-4V for Dermatological Diagnosis
This paper presents our team's participation in the MEDIQA-ClinicalNLP2024 shared task B. We present a novel approach to diagnosing clinical dermatology cases by integrating large multimodal models, specifically leveragi…
DiagnosticPrompt EngineeringRetrievalFederated Self-Supervised Contrastive Learning and Masked Autoencoder for Dermatological Disease Diagnosis
In dermatological disease diagnosis, the private data collected by mobile dermatology assistants exist on distributed mobile devices of patients. Federated learning (FL) can use decentralized data to train models while k…
Contrastive LearningFederated LearningSelf-Supervised LearningPrototypical Clustering Networks for Dermatological Disease Diagnosis
We consider the problem of image classification for the purpose of aiding doctors in dermatological diagnosis. Dermatological diagnosis poses two major challenges for standard off-the-shelf techniques: First, the data di…
ClusteringGeneral Classificationimage-classificationImage Classification