Explainable ViT Skin-Lesion Pipeline

I’m finalising a research study on explainable Vision Transformers for dermoscopic skin-lesion analysis and need a working, fully documented pipeline delivered within one week.

Core workflow
• Hair removal: start from an existing DM–AHR diffusion model, adapt it to the HAM10000 images, and make the code modular so I can swap parameters or update checkpoints later.
• Comparison set-up: for every lesion, keep the untouched image, run it through several classical hair-removal techniques, and generate the DM–AHR result.
• Classification: train and test a separate ViT model on each image version (original, every classical method, DM–AHR) so the performance impact of hair removal is crystal-clear.
• Metrics & reporting: compute Accuracy, Precision, Recall, F1-score, Sensitivity, Specificity, PSNR and SSIM, then collate them in tables and clear plots.
• Explainability: supply Grad-CAM heatmaps and ViT attention maps for correctly and incorrectly classified samples, with concise commentary.

Deliverables
– Google Colab notebook(s) that run end-to-end with a single click (dataset download, preprocessing, training, evaluation, visualisation).
– Well-structured source code and helper scripts.
– Saved model checkpoints and log files.
– PDF/Markdown documentation explaining each step, key parameters, and how to reproduce every figure in the manuscript.
– A brief video walkthrough—or annotated notebook cells—so I can confidently present and defend the methodology.

Please let me know:
1. Your direct experience adapting DM–AHR or similar diffusion models.
2. How you will meet the seven-day deadline without sacrificing reproducibility.

I’m ready to award immediately; looking forward to your plan.