Notes from Industry

Developing methods towards better deep brain stimulation treatments.

Authors: Jordan Taylor and Melvyn Yap (Neuroscience Research Team at Max Kelsen)

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Image edited from https://unsplash.com/photos/IHfOpAzzjHM. Thanks to Robina Weermeijer.

We used the activity of a few neurons in the brains of rats to predict the timing of audio beeps they were hearing. Hopefully, the models and insights we developed for this problem may eventually be useful on the path towards improving treatments for Parkinson’s disease and other neurological disorders.

Background

Deep brain stimulation is being used to help alleviate the symptoms of severe Parkinson’s disease, dystonia, and epilepsy (with recent promising results in treating severe depression). It involves inserting electrodes into specific parts of the brain, as shown…


Model Interpretability

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Photo by rishi on Unsplash

In our previous blog, we introduced the concept of SHAP values (4) and its advantages over other saliency mapping methods such as LIME (5). We also proposed that the application of this method to genetic data can be used to explore biology and enable clinical utility of deep models. However, given the reliability issues touched on in the previous blog as well as those reported in (1, 9) it is absolutely paramount that we tread carefully before putting our trust in the interpretations of deep learning results. …


Model Interpretability

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Photo by Markus Spiske on Unsplash

The road to the practical application of Machine Learning (ML) to medical data has been a long one and finally, the end may be in sight! It has already been successfully applied to things such as stained tumour tissue microarray (TMA) samples (Bychkov et al, 2018), whole-slide images (Ehteshami Bejnordi, 2017), and skin cancer images (Haenssle et al, 2018) with great success in terms of diagnostic accuracy, and at times, even outperforming clinical experts! However, before it can be truly implemented in the wider field of healthcare and have the ability to change people’s lives, we must first cross our…


Author: Navid Toosi Saidy, Quality and Technology Translation Lead at Max Kelsen

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Image Credit: FDA Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) Action Plan

At Max Kelsen, we’re developing a range of advanced machine learning-based Digital Health solutions for a range of pressing issues across the healthcare and life sciences industry. We envision this next generation of software tools will be vital to improving healthcare services, diagnostics and disease or injury management and assist healthcare workers to provide the best quality of care to patients.

Software used for diagnostics, prevention, treatment or monitoring of a disease fall into a category called ‘software as a medical device’ (SaMD) and are closely monitored and…


Author: Melvyn Yap, PhD, Senior Machine Learning Researcher at Max Kelsen

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The Elusive Research Path

Completing a PhD (Doctor of Philosophy) requires an incredible amount of focus, persistence, personal sacrifices, and quite often, a healthy dose of pure luck. It is common belief that earning the highest possible level of academic qualification will surely and automatically translate to securing a research job, and that your life is guaranteed to be stable and financially rewarding. But is it?

The brutal reality is that the vast majority of PhD graduates do not end up continuing a career in research, let alone becoming an independent researcher. This…


Authors: Kaiah Steven, Quantum Machine Learning Researcher & Jacob O’Farrell, Principal Engineer at Max Kelsen.

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Max Kelsen is only one of three Australian companies to attain AWS’s machine learning competency status, and was named AWS’s 2020 Partner of the Year for Data, Analytics and Machine Learning. We are constantly in the thick of upskilling our team by attending conferences that fuel our thirst for knowledge and keeping our curiosity alive.

AWS re:Invent is a free 3-week virtual conference held by AWS as an opportunity for AWS experts to lead sessions and share the latest news, technologies, and trends within the…


Author: Jacob O’Farrell, Technical Delivery Lead at Max Kelsen

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Max Kelsen is only one of three Australian companies to attain AWS’s machine learning competency status, and was named AWS’s 2020 Partner of the Year for Data, Analytics and Machine Learning. We are constantly in the thick of upskilling our team by attending conferences that fuel our thirst for knowledge and keeping our curiosity alive.

AWS re:Invent is a free 3-week virtual conference held by AWS as an opportunity for AWS experts to lead sessions and share the latest news, technologies, and trends within the space. The Max Kelsen team are…


Author: Dr Maciej Trzaskowski (Research Lead at Max Kelsen)

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Photograph: DeepMind/PA

In 2018 and currently in 2020, DeepMind submitted their newly developed AlphaFold and AlphaFold 2 AI-based model to Critical Assessment of Techniques for Protein Structure Prediction (CASP13). CASP is an independent community wide experiment that invites participants every two years to test their predictive models on experimental proteins that are not yet publicly available. The aim is to predict 3D structures of these new proteins from amino acid sequences. …


Exploring existing financial mathematics problems where quantum amplitude estimation & algorithms are useful.

Author: Thasmika Gokal (Machine Learning Engineer, Max Kelsen) & Luke Kamols (Quantum Research Intern, Max Kelsen)

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In recent years alone, Wall Street titans, such as JP Morgan and Goldman Sachs, generated a cumulative revenue of half a trillion dollars. Quantitative analysts — or “quants” — have been instrumental to this tremendous success. Not only can they predict the expected payoff of financial derivatives using classical algorithms, “quants” are also able to estimate the level of financial risk.

According to McKinsey & Company, the finance sector is the most likely industry to significantly reap the rewards from quantum computing in the…


Final takeaways and highlights from KubeCon’s bi-annual conference, held virtually for the very first time — part four of a four part series.

Authors: Ruby Nguyen, Marketing Coordinator at Max Kelsen, and Jacob O’Farrell, Technical Delivery Lead at Max Kelsen.

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The final day of KubeCon has come and gone and we have been really blown away by the quality of presentations by all industry thought leaders involved. We would like to thank everyone who took their time to disseminate their knowledge at this year’s conference. Going virtual proved to be an extremely flexible format that enhanced the communication available between the attendees and speakers — a priceless experience!

Some final takeaways and thoughts from the Max Kelsen team who attended this year’s virtual…

Max Kelsen

We are an Artificial Intelligence and Machine Learning consultancy that delivers competitive advantage for government and enterprise. https://maxkelsen.com

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