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    <title>Going full Python for Machine Learning in Biomedical engineering</title>

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    <h3>Going full Python for Machine Learning in Biomedical engineering</h3>
    
    <h1>Going full Python for Machine Learning in Biomedical engineering</h1>
<p>The talk with cover the following reflexions coming from the application:
First we need to address a set of constrains coming from the Biomedical application:
  - Data is expensive but dimensions are many: gathering experimental data on humans is complicated (ethics, recruiting subjects, performing actual measurements…) but then each recording holds  a lot of informations (tens of EMG channels at up to 4kHz, accelerometers, motion capture…) and many features can be extracted from that raw data.
  - Robustness is important to address early as there can be a high variability of the signals recorded between subjects but also for the same subject at different times or in slightly different conditions (for example just misplacing electrodes)
  - Supervision of algorithm can be tricky: specially in absence of objective gold standard, then categorization of subjects is often subjective (type/stage of pathology established only by clinical score or MD appreciation)
  - Accuracy is critical in medical application: Sensitivity and Specificity must be as high as possible as  false negative would mean failing to treat and false positive treating healthy patients.
  - Finally, the whole process has to be fully automated and fast to fit within existing procedures and time frames.</p>
<p>We choose to go full Python for our developments in this project for Python versatility and efficiency:
  - We need to test and prototype many methods to find the most suitable ones: <em>scipy</em> and <em>scikit</em> learn have already implemented most components we might need. Hence we can focus on application specific aspects.
  - We need to communicate with non developers (experimenters, MD,…) at various stages with plots, graphical interfaces or even dynamics websites: this can be covered easily in Python.
  - We wish for our code to take advantage of currently available hardware: parallel computation and GPU usage can be easily prototyped with <em>Numba</em>, <em>Dask</em> or <em>TensorFlow/CAFFE</em>.
  - Finally, Python enables us to address all these task with a single language, easy to learn for interns/students hired within the project.</p>
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