Dr James Howard

Academic Clinical Lecturer and Cardiac Imaging Fellow at Imperial College London.
PhD in convolutional neural networks.

Kaggle SETI Breakthrough Listen challenge

I achieved my second Kaggle gold medal (and my first solo) in the SETI Breakthrough Listen challenge. Fascinating competition using AI to process radio telescope signals. Mini write-up here https://kaggle.com/c/seti-breakthrough-listen/discussion/266460

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How convolutional neural networks work

Interested in how these "deep learning" systems appearing in your echo/CMR/CT software actually work? Our review in @Heart_BMJ explains what's going on "under the hood" in neural networks, with a focus on medical imaging: https://heart.bmj.com/content/early/2021/07/23/heartjnl-2020-318686

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Echocardiography AI

Our AI collaborative's first paper is out in @circimaging! We trained an AI to perform measurements of parasternal long axis #echofirst images, using expert opinions from cardiologists and physiologists from 17 hospitals across the UK. But hasn't this been done before? Well, yes... but not…

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Cardiac MRI

We investigated whether deep learning of the sequences acquired in the first minutes of a scan could provide an early alert of abnormal features. It does this by segmenting out slices of the heart acquired at the start of a scan, and using these to…

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Deepfake detection challenge

Ecstatic to say our 2-man team ended up finishing 5th in Kaggle's and Facebook's DeepFake Detection Challenge! It was my first AI competition, and the $40,000 prize was a wonderful surprise!

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Arterial waveforms

The identification of adverse pressure signals during an angiogram, termed "damping", is necessary to maintain safety and the accuracy of coronary physiology measurements. We developed a convolutional neural network which is able to accurately monitor these signals. This work has been published in JACC: Cardiovascular Interventions

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Pacemakers

We've developed a free-to-use tool utilising a convolutional neural network which is able to accurately identify the model of pacemaker present on a chest X-ray. This work has been published in JACC: Clinical Electrophysiology (open access).

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Echocardiography AI

Automated echocardiographic interpretation hinges on the correct recognition of the view (imaging plane and orientation). Current state-of-the-art methods for identifying the view computationally involve 2-dimensional convolutional neural networks (CNNs) and ignore information describing the movement of structures throughout the cardiac cycle. Here we explore…

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