DermNet: 偏見軽減のための統合型CNN-ViTアーキテクチャを用いた皮膚科診断における高度な教師なし病変セグメンテーション

In this paper, we propose a method for reducing the bias in skin disease identification for people of color with the aid of lesion only zero shot unsupervised approach that is then passed to the classifier Dermnet comprising of a hybrid Vision Transformer and Convolutional Neural Network, achieving robust validation accuracy of approximately 81%. Our Segmentation without training with labeled data as is the case with traditional U-Net has achieved an IOU of 90% across all skin colors in segmenting the lesion from skin effectively eradicating the impact of skin in the classification of disease.