DERM is different to other AI
The machine learning algorithm built by Skin Analytics, Deep Ensemble for the Recognition of Malignancy (DERM), recognises the most common malignant, pre-malignant, and benign skin lesions. This includes melanoma, the most dangerous of the common skin cancers.
Almost all existing solutions for the recognition of skin cancer in images take a similar approach: using an existing ‘pre-trained’ neural network, and retraining it using skin lesion data. These ‘pre-trained’ neural networks (such as Google’s Inception network and Microsoft Research’s ResNet) are designed to perform very different tasks, such as large-scale image recognition (classification of 1000s of image categories, such as cats, dogs, and lamp-posts). While use of this approach gives a reasonable performance for an initial proof-of-concept, a solution of this type is inadequate for deployment in a medical device.
At Skin Analytics we’ve taken a different approach, designing all aspects of our machine learning architecture from the ground up for the specific problem we are trying to solve. This includes specifically tailored machine learning architectures, training methodology, and data augmentation for detection of skin cancer. Award