This software contains a modification of the toolbox MatConvNet (http://www.vlfeat.org/matconvnet/). MatConvNet is a toolbox that implements CNN on Matlab.
This code implements our solution for automatic melanoma diagnosis, which has been submitted to the ISIC 2017 challenge in melanoma diagnosis (https://challenge.kitware.com/#challenge/583f126bcad3a51cc66c8d9a).
We have participated in the Part 3: Lesion Classification. In this task, participants are asked to complete two independent binary image classification tasks that involve three unique diagnoses of skin lesions (melanoma, nevus, and seborrheic keratosis). In the first binary classification task, participants are asked to distinguish between (a) melanoma and (b) nevus and seborrheic keratosis. In the second binary classification task, participants are asked to distinguish between (a) seborrheic keratosis and (b) nevus and melanoma.
Definitions:
Melanoma – malignant skin tumor, derived from melanocytes (melanocytic)
Nevus – benign skin tumor, derived from melanocytes (melanocytic)
Seborrheic keratosis – benign skin tumor, derived from keratinocytes (non-melanocytic)
A brief description of the method is given in:
Iván González-Díaz. Incorporating the Knowledge of Dermatologists to Convolutional Neural Networks for the Diagnosis of Skin Lesions. CoRR abs/1703.01976 (2017)
By the moment, I am providing just a demo to execute our diagnosis system over data. As soon as I clean the code, scripts for training will be provided.
To run the demo, follow these steps:
- Compite the MatConvNet toolbox as described in the authors webpage http://www.vlfeat.org/matconvnet/.
- In demo.m script, modify the corresponding parameters (useGPU,bsize, etc.)
- Run the demo.m script.
The code operates as follows:
1.- It accepts as inputs pair of images containing both the dermoscopic lesion and a binary lesion mask separating pixels belonging to the lesion to the surrounding skin.
2.- It performs a data augmentation process that generates rotated and cropped versions of the original image of the lesion.
3.- It generates a segmentation of the lesion into a set of 8 dermoscopic features of interest for dermatologists.
4.- Using the previous information, it computes a diagnosis as a 3 vector containing probabilities of benign, melanoma and seborrheic keratosis. As we have several views of the same lesion due to data augmentation,individual outputs are fused using an average aggregator before applying the softmax.