IViT applies quadratic programming to a pre-trained Vision Transformer with a multi-objective loss, achieving 93.80% accuracy on six skin disease datasets (0.21% below baseline) while reducing feature redundancy by 29.5% and producing clinically consistent activations.
A Deep Learning Approach Based on Explainable Artificial Intelligence for Skin Lesion Classification,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2representative citing papers
PEFT-MedSAM adapts MedSAM by training only its mask decoder on ISIC 2018 skin lesion data, achieving Dice 0.9411 and outperforming U-Net (0.8715) and zero-shot MedSAM (0.8997), with PH2 validation (0.9467) and 98.27% Grad-CAM pointing accuracy.
citing papers explorer
-
IViT: A Novel Interpretable Visual Transformer for Skin Disease Detection
IViT applies quadratic programming to a pre-trained Vision Transformer with a multi-objective loss, achieving 93.80% accuracy on six skin disease datasets (0.21% below baseline) while reducing feature redundancy by 29.5% and producing clinically consistent activations.
-
PEFT-MedSAM: Efficient Fine-Tuning of Medical Foundation Models for Explainable Skin Lesion Segmentation
PEFT-MedSAM adapts MedSAM by training only its mask decoder on ISIC 2018 skin lesion data, achieving Dice 0.9411 and outperforming U-Net (0.8715) and zero-shot MedSAM (0.8997), with PH2 validation (0.9467) and 98.27% Grad-CAM pointing accuracy.