Blog

Machine learning model deployment using AWS Lambda

The use of modern cloud services offers interesting opportunities in terms of cost, administrative ease and scalability. You only pay for the number of seconds your code is actually running, which makes it in general much cheaper than hosting your own server 24/7.

Pascal Niville

April 7

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4 min

Using Neural Networks for Invoice Recognition

About two years ago, we started developing a machine learning model for named entity recognition (NER) on invoices. With more data available now, we believe it is possible to make a big leap in performance by changing our AI model architecture. To achieve state-of-the-art performance, it is necessary to combine information from content and layout. To tackle this, we use an artificial neural network trained on our invoice dataset.

Ward Van Laer

Mar 30

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3 min read

Using Machine Learning for EEG seizure detection

Approximately 50 million people worldwide have epilepsy, making it one of the most common neurological diseases globally. In one of our recent projects for the Ghent University Hospital, we had to tackle the problem of detecting epileptic seizures in EEG signal recordings of rats and mice. In this project, we go one step further than existing epilepsy detection projects; we detect the exact start and end time of an epileptic seizure.

Ward Van Laer

Mar 30

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3 min read

Exploiting feature maps to increase end-user trust

To improve the end-users trust, Ixor adopts the information provided by intermediate results (feature maps) of the model to give more context to the prediction. The key idea is that similar inputs generate similar feature maps. The similarity of these can be calculated and when stored we have a database of how similar inputs are to each other.

Pascal Niville

Feb 12

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4 min

How to Setup an Active Learning Framework for your Object Detection Model

At IxorThink a simple framework for using active learning in object detection has been proposed to significantly save time and resources on the labeling efforts to train an object detection model. In addition to that, the proposed work proved to be effective in improving the model’s performance after the production stage.

Moustafa Ayoub

Jan 22

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6 min read

Using Deep Q-Learning in the Classification of an Imbalanced Dataset

One of the most common problems that can be faced when using machine learning is having an imbalanced dataset. Multiple approaches have been used to address this problem, whether on the algorithm’s level or data level. This was particularly a problem in out datasets of medical scans.

Moustafa Ayoub

Jan 22

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3 min

Stochastic patch generation for Whole Slide Imaging

Following the rise of deep learning, AI is making its way into the medical sector. A recent study showed that AI could detect breast cancer based on screening mammograms with comparable accuracy as expert radiologists. The study was able to achieve these excellent results not only because of the model development by Google Deepmind AI, but also because of a dataset that consisted out of almost 29,000 mammograms.

Lukas Verret

Jan 14

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5 min read