Conditional Image Generation with PixelCNN Decoders – slides

Awhile ago I presented and attempted to explain this work to our reading group:

van den Oord, A., Kalchbrenner, N., Vinyals, O., Espeholt, L., Graves, A., & Kavukcuoglu, K. (2016). Conditional Image Generation with PixelCNN Decoders. In D. D. Lee, M. Sugiyama, U. V Luxburg, I. Guyon, & R. Garnett (Eds.), NIPS (pp. 4790–4798). Retrieved from http://arxiv.org/abs/1606.05328

And also dived a bit into their previous work,
van den Oord, A., Kalchbrenner, N., & Kavukcuoglu, K. (2016). Pixel Recurrent Neural Networks. Arxiv, 48. Retrieved from http://arxiv.org/abs/1601.06759

While I usually post slides to the web shortly after, this time I’ve been scared to do so. There are a few critical points from this paper that I still don’t understand. And while I told myself that I would spend some time to figure this out, it is now months later, and I’ve taken no action. So as now is always the time to continue on in spite of the fear, I’ll let you, dear Internet, have these slides in all there erroneous ways.

Continue reading “Conditional Image Generation with PixelCNN Decoders – slides”

Convolutional Neural Networks for Adjacency Matrices

We had our work, BrainNetCNN, published in NeuroImage awhile ago,

Kawahara, J., Brown, C. J., Miller, S. P., Booth, B. G., Chau, V., Grunau, R. E., Zwicker, J., G., Hamarneh, G. (2017). BrainNetCNN: Convolutional neural networks for brain networks; towards predicting neurodevelopment. NeuroImage, 146(Feb), 1038–1049. http://doi.org/10.1016/j.neuroimage.2016.09.046

and I’ve meant to do a blog writeup about this. We recently released our code for BrainNetCNN on GitHub (based on Caffe), which implements the proposed filters designed for adjacency matrices.

We called this library Ann4Brains. In hindsight, we could have called this something more general and cumbersome like Ann4AdjacencyMatrcies, but I still like the zombie feel that Ann4Brains has.

We designed BrainNetCNN specifically with brain connectome data in mind. Thus the tag line of,

“Convolutional Neural Networks for Brain Networks”

seemed appropriate. However, after receiving some emails about using BrainNetCNN for other types of (non-connectome) data, I’ll emphasize that this approach can be applied to any sort of adjacency matrix, and not just brain connectomes.

The core contribution of this work is the filters designed for adjacency matrices themselves. So we’ll go through each of them. But first, let’s make sure we are clear on what the brain connectome (or adjacency matrix) is.

Continue reading “Convolutional Neural Networks for Adjacency Matrices”

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”

multi-resolution-tract CNN with hybrid pretrained and skin-lesion trained layers

Our paper entitled: “Multi-resolution-Tract CNN with Hybrid Pretrained and Skin-Lesion Trained Layers” was accepted and presented as an oral talk in the Machine Learning in Medical Imaging (MLMI) Workshop (part of the MICCAI conference).

In this work, we used a convolutional neural network (CNN) to classify 10 different types of skin lesions, including melanoma and non-melanoma types.

The key technical contribution was to use multiple tracts (or paths) within the neural network, to train (and test) the network on an image using multiple image resolutions simultaneously. Additionally, we extended a CNN pretrained on a single image resolution to work for multiple image resolutions.

Here are our slides presented at MLMI (thanks Aïcha!) showing our deep learning approach to classify skin disease:
Continue reading “multi-resolution-tract CNN with hybrid pretrained and skin-lesion trained layers”

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”

How to segment the spinal cord and predict disability in multiple sclerosis patients

So I have officially finished my Master’s work 🙂 and I’ve written up a more extended abstract of the thesis with less technical terms below. This hopefully can give you some concise intuition on the work without the technical details. This page is meant to be a summary of all things related to this work and links to other relevant articles that I wrote.

The thesis is available here and is entitled:
Spinal cord segmentation and disability prediction in multiple sclerosis using novel optimization and machine learning methods

This thesis examines a) how to segment the spinal cord from 3D magnetic resonance images (MRI) using computer-aided methods, and b) how to predict the level of clinical disability in multiple sclerosis (MS) patients using spinal cord segmentations and corresponding MRIs.

Continue reading “How to segment the spinal cord and predict disability in multiple sclerosis patients”

Predicting Disability in Patients with Multiple Sclerosis using MRI – MICCAI CSI

Our work on predicting the physical disability level of patients was accepted for an oral presentation at the MICCAI 2013 workshop on, Computational Methods and Clinical Applications for Spine Image, in Nagoya, Japan.

The paper itself you can read here and is called:
Novel morphological and appearance features for predicting physical disability from MR images in multiple sclerosis patients