Automate the diagnosis of Knee Injuries with Deep Learning part 1: an overview of the MRNet Dataset

By Ahmed Besbes Posted on Mar 25 juin 2019 in Medical Imaging • Tagged with MRI, Medical Imaging, Computer Vision, MRNet, Convolutional Neural Networks, PyTorch, image classification, Jupyter WidgetsLeave a comment

If you are interested in learning an impactful medical application of artificial intelligence, this series of articles is the one you should looking at.
My goal is to show you how you can use deep learning and computer vision to assist radiologists in automatically diagnosing severe knee injuries from MRI scans.
To do this, we'll first explore the MRNet dataset in this first post. We'll then build a deep learning classification model in PyTorch in the next post and develop an interpretation pipeline in the last one.
By the end, you'll have an overview of a medical imaging application with different components that you can use elsewhere in similar situations.
Let's start.

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Overview and benchmark of traditional and deep learning models in text classification

By Ahmed Besbes Posted on Mar 12 juin 2018 in Sentiment Analysis • Tagged with NLP, CNN, RNN, GRU, transfer learning, deep learning, keras, neural networks, Twitter, GloVe, Bag of words, word ngrams, character ngramsLeave a comment

This article is an extension of a previous one I wrote when I was experimenting sentiment analysis on twitter data. Back in the time, I explored a simple model: a two-layer feed-forward neural network trained on keras. The input tweets were represented as document vectors resulting from a weighted average of the embeddings of the words composing the tweet. The embedding I used was a word2vec model I trained from scratch on the corpus using gensim. The task was a binary classification and I was able with this setting to achieve 79% accuracy.
The goal of this post is to explore other NLP models trained on the same dataset and then benchmark their respective performance on a given test set. We'll go through different models: from simple ones relying on a bag-of-word representation to a heavy machinery deploying convolutional/recurrent networks: We'll see if we'll score more than 79% accuracy!
Let's investigate !

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Understanding deep Convolutional Neural Networks with a practical use-case in Tensorflow and Keras

By Ahmed Besbes Posted on Lun 13 novembre 2017 in Computer Vision • Tagged with Deep learning, Convolutional Neural Networks, Image Classification, Keras, Tensorflow, AWS, GPULeave a comment

Convolutional Neural Networks (CNNs) are nowadays the standard go-to technology when it comes to analyzing image data. These are special neural network architectures that perform extremely well on image classification. They are widely used in the computer vision industry and are shipped in different products: self driving cars, photo tagging systems, face detection security cameras, etc.
The theory behind convnets is beautiful. It attempts to explain and reverse-engineer the vision process. In this article, I'll go through it and explain what CNNs are all about. I'll try to go over the hype you see on the mass media and provide a detailed explanation with code snippets and interpretations.
This is also a hands-on guide to setup a deep learning dedicated machine on AWS and develop an end-to-end CNN model from scratch using Keras and Tensorflow.
By the end of this post you should have the global picture about CNNs: How do they work? and How to put them in practice?

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Sentiment analysis on Twitter using word2vec and keras

By Ahmed Besbes Posted on Jeu 20 avril 2017 in Sentiment Analysis • Tagged with NLP, word2vec, doc2vec, Deep Learning, Keras, Neural Networks, TwitterLeave a comment

The focus of this post is sentiment analysis. This is a Natural Language Processing (NLP) application I find challenging but enjoyable. It aims at identifying emotional states, reactions and subjective information. It tries to determine the attitude of a speaker with respect to some topic.
If done automatically with high precision and on a large scale, sentiment analysis could be a goldmine for marketers or politicians who want to measure the public opinion through social networks.
In this post I'll show you how I built a machine learning model that classifies tweets with respect to their polarity. Tweets are short and yet capture lots of subjective information. That's why we'll be playing with them.
Some words for those who are ready to dive in the code: I'll be using python, gensim, the word2vec model and Keras.

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How to mine newsfeed data and extract interactive insights in Python

By Ahmed BESBES Posted on Mer 15 mars 2017 in Topic Mining • Tagged with tf-idf, LDA, Kmeans,, NLP, Topic mining, Text Clustering, BokehLeave a comment

In this tutorial we'll dive in Topic Mining. We'll analyze a dataset of newsfeed extracted from more than 60 sources. We'll show how to process it, analyze it and extract visual clusters from it. We'll be using great python tools for interactive visualization, topic mining and text analytics.
All the code is available to you to run and test. No bullshit.

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