Mastering the Game of Go – slides [paper explained]

This week I presented to our weekly reading group, this work:

Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., … Hassabis, D. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), 484–489.

To quickly summarize this work…

Basically, they create a policy network, which is a convolutional neural network, that predicts the next move a human player would do from a board state. They create a value network, also a convolutional neural network, that predicts the outcome (win or lose) of the game given the current board state.
Continue reading “Mastering the Game of Go – slides [paper explained]”

Dermofit 10-class – differences in ISBI and MLMI accuracy explained

I just got a great question asking why there is a discrepancy in the accuracy reported in our two works:

[ISBI paper, we report 81.8% accuracy over 10 classes]
Kawahara, J., BenTaieb, A., & Hamarneh, G. (2016). Deep features to classify skin lesions. In IEEE ISBI (pp. 1397–1400). Summary and slides here.

[MICCAI MLMI paper, we report 74.1% accuracy over 10 classes]
Kawahara, J., & Hamarneh, G. (2016). Multi-Resolution-Tract CNN with Hybrid Pretrained and Skin-Lesion Trained Layers. In MLMI. Summary and slides here.

We use the same Dermofit dataset, so it seems surprising the accuracy we report in the papers are different. So I thought I would elaborate on why here.
Continue reading “Dermofit 10-class – differences in ISBI and MLMI accuracy explained”

Deep features to classify skin lesions – summary and slides

Here I'm nervously just starting our talk on our approach to skin lesion classification
Me nervously just starting to talk about our approach to skin lesion classification.

We presented our work, “Deep Features to Classify Skin Lesions” at ISBI 2016 in Prague! And I’m happy to report that our work was awarded runner-up for the Best Student Paper Award 🙂

In this work, we looked at how to classify skin lesions from images captured with a digital camera (i.e., non-dermoscopy). Our approach was able to distinguish among 10 different types of skin diseases over 1300 images and achieved an accuracy higher than what was previously reported over the same dataset. We did this by applying deep learning (i.e., pretrained convolutional neural networks) to melanoma and non-melanoma skin images.

Continue reading “Deep features to classify skin lesions – summary and slides”