MODULE 01 · DAYS 1–3
Foundations
“Building the Base — Medicine Meets Machine Intelligence”
Dermatology anatomy, clinical diagnosis workflows, computer vision fundamentals, and production-grade data engineering pipelines for medical images.
AI Diagnosis of Melanoma, Psoriasis & Rare Skin Disorders
A 15-day intensive workshop teaching you to build, fine-tune, explain, and deploy state-of-the-art Vision Transformer models for clinical skin disease diagnosis — using entirely free tools and real medical datasets.
📌 Quick Answer — What is a Vision Transformer in Dermatology?
A Vision Transformer (ViT) is a deep learning architecture that divides a skin image into fixed-size patches (e.g., 16×16 pixels), embeds them as tokens, and uses self-attention mechanisms to capture long-range spatial relationships across the entire image — enabling highly accurate diagnosis of conditions like melanoma, psoriasis, eczema, and rare genodermatoses from dermoscopic or clinical photographs. Unlike CNNs, ViTs excel at capturing global context, making them the current gold standard for large-scale dermatology AI.
From dermatology fundamentals through clinical deployment — every module is designed for job-market readiness.
MODULE 01 · DAYS 1–3
“Building the Base — Medicine Meets Machine Intelligence”
Dermatology anatomy, clinical diagnosis workflows, computer vision fundamentals, and production-grade data engineering pipelines for medical images.
MODULE 02 · DAYS 4–6
“The Architecture That Changed Medical Imaging”
Deep-dive into ViT, DeiT, Swin Transformer, and BEiT architectures. Fine-tuning strategies, loss functions, and benchmarking for dermatology tasks.
MODULE 03 · DAYS 7–8
“A Black Box Has No Place in a Clinic”
Grad-CAM heatmaps, attention rollout, SHAP attributions, and clinical-grade evaluation metrics including sensitivity, specificity, AUC, and calibration.
MODULE 04 · DAY 9
“AI That Works for Every Skin Tone”
Bias detection across Fitzpatrick skin types, disparate impact analysis, fairness constraints, and mitigation strategies for equitable dermatology AI.
MODULE 05 · DAYS 10–12
“Beyond Images — The Full Clinical Picture”
Multimodal fusion of images and metadata, few-shot learning for rare disorders, CLIP zero-shot classification, and pixel-level lesion segmentation.
MODULE 06 · DAYS 13–14
“From Notebook to Clinic”
ONNX optimization, Gradio clinical demos, FastAPI REST endpoints, HIPAA compliance basics, model drift monitoring, and FDA SaMD regulatory pathways.
MODULE 07 · DAY 15
“Build Your Portfolio. Land Your Job.”
End-to-end capstone project: fine-tuned Swin Transformer on HAM10000/ISIC 2020, Grad-CAM explainability, fairness audit, Gradio deployment, and Hugging Face model card. Full career roadmap for Medical AI roles.
60–90 minutes per day. Click any day to expand the full lecture details.
What Every AI Engineer Must Know About Skin
🧠 Theory Covered
🛠 Tools & Datasets
💼 Job Relevance: Clinical AI roles require domain knowledge. Understanding dermatology terminology and diagnostic workflows is essential for Medical AI Engineer, Clinical Data Scientist, and Health Informatics roles at companies like Tempus, Flatiron Health, and PathAI.
From Pixels to Predictions — The Visual AI Pipeline
🧠 Theory Covered
🛠 Tools & Datasets
💼 Job Relevance: Every Computer Vision Engineer and ML Engineer interview includes image preprocessing and tensor manipulation. Building this skill directly on medical data is a strong portfolio signal.
Garbage In, Garbage Out — Building Clean Medical Image Pipelines
🧠 Theory Covered
🛠 Tools & Datasets
💼 Job Relevance: Data pipelines are the #1 skill gap in medical AI hiring. Albumentations + class imbalance handling is explicitly listed in ML Engineer JDs at Tempus, Flatiron Health, and PathAI.
Attention Is All You Need — Now for Skin
🧠 Theory Covered
🛠 Tools & Hands-On
💼 Job Relevance: ViT architecture knowledge is tested in ML Engineer and Research Scientist interviews at Google Health, Microsoft Azure AI, and medical imaging startups. Explaining attention mechanisms clinically is a top differentiator.
Standing on the Shoulders of Giants — Adapting Pretrained Models
🧠 Theory Covered
🛠 Tools & Datasets
💼 Job Relevance: Fine-tuning pretrained models is the most common task in industry medical AI. WandB proficiency is explicitly listed in ML Engineer JDs at Recursion Pharmaceuticals, Owkin, and Paige.AI.
Choosing the Right Transformer for Dermatology
🧠 Theory Covered
🛠 Tools & Datasets
💼 Job Relevance: Benchmarking and selecting optimal architectures is a core Research Engineer skill at Skin Analytics, MetaDerm, and DermTech.
Making the Model Explain Itself to a Dermatologist
🧠 Theory Covered
🛠 Tools & Hands-On
💼 Job Relevance: XAI is mandatory for FDA 510(k) clearance. Grad-CAM and SHAP are required skills in Clinical AI Scientist roles at J&J MedTech, Siemens Healthineers, and GE Healthcare.
Accuracy Is a Lie — What Really Matters in Clinical Settings
🧠 Theory Covered
🛠 Tools & Hands-On
💼 Job Relevance: Clinical AI validation is regulated. Sensitivity/specificity trade-offs are essential for Regulatory Affairs Scientist and Medical Device Software Engineer roles.
When the Algorithm Fails Dark Skin — Understanding & Fixing Bias
🧠 Theory Covered
🛠 Tools & Datasets
💼 Job Relevance: Health equity in AI is now a regulatory and ESG requirement. AI Ethics Researcher and Responsible AI Lead roles are among the fastest-growing in health tech.
The Dermatologist Uses More Than Their Eyes — So Should Your Model
🧠 Theory Covered
🛠 Tools & Datasets
💼 Job Relevance: Multimodal AI is the frontier of clinical AI. Research Scientist roles at DeepMind Health, Microsoft Research, and Owkin specifically require multimodal fusion experience.
Teaching AI to Recognize What It Has Barely Seen
🧠 Theory Covered
🛠 Tools & Datasets
💼 Job Relevance: Rare disease AI is a $50B+ market. Orphan drug companies and rare disease biotech firms actively hire ML Engineers with few-shot learning expertise.
Where Exactly Is the Lesion?
🧠 Theory Covered
🛠 Tools & Datasets
💼 Job Relevance: Segmentation is required for Medical Imaging AI roles at Philips Healthcare, Viz.ai, Aidoc, and Caption Health. Dice Score is a standard interview topic.
Making Your Model Fast Enough for Real-Time Dermatology
🧠 Theory Covered
🛠 Tools & Hands-On
💼 Job Relevance: MLOps and deployment skills are the #1 gap in medical AI candidates. ML Engineer and Clinical AI Platform Engineer roles all require ONNX, FastAPI, and regulatory deployment knowledge.
Deploying Is Day 1 — Maintaining Is the Real Job
🧠 Theory Covered
🛠 Tools & Hands-On
💼 Job Relevance: Healthcare MLOps Engineer roles at Epic Systems, Cerner (Oracle Health), and AWS HealthLake command premium salaries and are rapidly growing.
Build Your Portfolio. Land Your Job.
🎓 Capstone Deliverables
💼 Career Roadmap Covered
🏆 Final Output: A complete, documented, deployed dermatology AI system — ready to show in interviews, add to GitHub, and publish on Hugging Face Hub as your flagship medical AI portfolio project.
All datasets used in this workshop are publicly available and free for academic and research use.
| Dataset | Images | Classes | Primary Use | Workshop Days | Cost |
|---|---|---|---|---|---|
| HAM10000 | 10,015 | 7 skin lesion types | Core classification & multimodal pipeline | Days 3, 5, 8, 10, 15 | ✓ Free |
| ISIC 2018 | 10,015 | 7 + segmentation masks | Lesion segmentation task | Day 12 | ✓ Free |
| ISIC 2019 | 25,331 | 8 classes | Large-scale classification fine-tuning | Days 5, 6 | ✓ Free |
| ISIC 2020 | 33,126 | 2 + patient metadata | Melanoma detection + multimodal | Days 3, 10, 15 | ✓ Free |
| PH2 Dataset | 200 | 3 melanoma classes | Melanoma research + segmentation GT | Days 2, 12 | ✓ Free |
| SD-198 | 6,584 | 198 disease categories | Rare disorders + few-shot learning | Days 3, 6, 11 | ✓ Free |
| Fitzpatrick17k | 16,577 | 114 + Fitzpatrick type labels | Fairness audit & bias mitigation | Day 9 | ✓ Free |
| Derm7pt | 1,011 | 7-point checklist scoring | Clinical scoring validation | Day 6 | ✓ On Request |
| DermNet NZ | 23,000+ | 2,000+ conditions | Reference library + rare genodermatoses | Days 1, 11 | ✓ Educational |
Every tool used in this workshop is 100% free. Zero cost to complete the entire program.
This complete pipeline requires zero cost, runs entirely in the browser, and covers the full workflow from raw data to deployed clinical demo.
# ✅ Recommended Free Workshop Stack Environment: Google Colab # free T4 GPU Model: Hugging Face ViT # pretrained Timm Swin-T # fine-tune Data: HAM10000 # 10k images, 7 classes Fitzpatrick17k # fairness audit Augmentation: Albumentations # medical-grade XAI: Grad-CAM # heatmaps Fairness: Fairlearn # bias audit Deploy: Gradio # clinical demo app # Total cost: $199.00
Every lecture includes explicit job-market context for these roles.
Build and deploy clinical AI systems at companies like PathAI, Paige.AI, and DermTech.
Analyze medical imaging data, validate models, and generate clinical evidence at health systems.
Publish and advance ViT architectures for medical imaging at Google Health or Microsoft Research.
Own the ML infrastructure pipeline at Epic Systems, Cerner (Oracle Health), or AWS HealthLake.
Navigate FDA 510(k), SaMD, and EU AI Act pathways for medical AI product clearance.
Lead fairness, bias, and health equity research in AI systems at academic and industry labs.
Structured for featured snippet and AI answer engine optimization.
A Vision Transformer (ViT) splits skin images into 16×16 patches, applies multi-head self-attention across all patches, and classifies skin conditions like melanoma, psoriasis, eczema, and rare genodermatoses. Unlike CNNs, ViT captures global context across the entire image, making it superior for large-scale dermoscopy datasets.
The best free datasets are: HAM10000 (10,015 images, 7 classes), ISIC 2020 (33,126 images with metadata), Fitzpatrick17k (16,577 images with skin tone labels for fairness research), SD-198 (6,584 images across 198 rare categories), and PH2 (200 dermoscopic images with ground truth segmentation).
Zero-cost tools include: Google Colab (free T4 GPU), PyTorch, Hugging Face Transformers (ViT, Swin), Albumentations (augmentation), pytorch-grad-cam (explainability), Fairlearn (bias detection), and Gradio (deployment). The entire workshop can be completed with no paid tools.
Career paths include Medical AI Engineer, Clinical Data Scientist, Computer Vision Research Scientist, MLOps Engineer (Healthcare), Regulatory AI Specialist, and AI Ethics Researcher. Hiring companies include PathAI, Paige.AI, Google Health, Microsoft Health, DermTech, Skin Analytics, Tempus, and Owkin.
Grad-CAM (Gradient-weighted Class Activation Mapping) computes the gradient of the predicted class score with respect to the final convolutional feature map, then creates a heatmap highlighting which skin regions most influenced the AI’s decision — helping dermatologists verify that the model is focusing on clinically relevant lesion features.
Dermatology AI bias occurs when models perform significantly worse on darker Fitzpatrick skin types due to underrepresentation in training datasets. It is mitigated by: using diverse datasets (Fitzpatrick17k), applying re-sampling or re-weighting, using adversarial debiasing techniques, and auditing with fairness tools like Fairlearn and IBM AI Fairness 360.
15 days. 75 minutes per day. One complete, deployable dermatology AI system ready for your portfolio.