Melanoma is a dangerous form of skin cancer that can be life-threatening if not caught early. While dermatologists are trained to spot it, could AI models like CLIP help us identify melanoma from images — just like humans do when reading and seeing?
Let's explore! 👇
Early detection of melanoma saves lives. But:
- Not everyone has access to a dermatologist.
- Visual symptoms can be subtle.
- Misdiagnosis is common.
That's where AI for medical imaging can step in — and in this project, we explore multimodal learning using CLIP to do just that.
CLIP (Contrastive Language-Image Pretraining) is a vision-language model from OpenAI.
✅ Trained on 400 million image–text pairs,
✅ Learns to "connect" images with their textual descriptions,
✅ Can generalize across domains with zero-shot learning.
But can CLIP generalize to medical images, like skin lesions? That's the challenge.
VLMs like CLIP learn joint representations of text and images. Think of them as models that can:
- See an image 🖼️ and describe it in words 📝
- Read a phrase and find the most relevant image 🔍
We explore whether this multimodal power can classify melanoma images, guided by medical text descriptions.
We experiment with medical datasets such as:
- ISIC Archive: The International Skin Imaging Collaboration, a benchmark for skin lesion diagnosis.
- PH2 Dataset: A dermoscopic image dataset focused on melanocytic lesions.
- Test CLIP’s zero-shot classification ability on melanoma images.
- Fine-tune CLIP on a medical dataset.
- Compare performance with standard CNN classifiers.
- Explore prompt engineering to guide better detection.
- Melanoma causes over 50,000 deaths globally per year.
- AI could serve as a first-line diagnostic aid, especially in underserved areas.
- CLIP offers a flexible, generalizable baseline — without retraining from scratch.