renaudmarquis

renaudmarquis

Geek Repo

Company:CHUV

Location:Lausanne, Switzerland

Home Page:https://www.linkedin.com/in/renaud-marquis

Github PK Tool:Github PK Tool

renaudmarquis's repositories

conn-viz

OG 2019 - Project 09: Visualizing brain connectomics using D3.js

Language:JavaScriptLicense:GPL-3.0Stargazers:1Issues:0Issues:0

amld-workshop-pneumonia

Workshop about detecting pneumonia in X-Ray images

Language:Jupyter NotebookStargazers:0Issues:0Issues:0

desktop-app

One app to rule them all!

Language:TypeScriptLicense:Apache-2.0Stargazers:0Issues:0Issues:0

dual-EEG

Processing and interoperability for dual EEG project

Language:MATLABLicense:GPL-3.0Stargazers:0Issues:0Issues:0

eegdev

Biosignal acquisition device library

Language:CLicense:LGPL-3.0Stargazers:0Issues:0Issues:0

eegview

Minimal software to display in realtime and record EEG signals

Language:CLicense:GPL-3.0Stargazers:0Issues:0Issues:0

FIACH

Development version of FIACH

Language:RStargazers:0Issues:0Issues:0

mnelab

MNELAB - a graphical user interface (GUI) for MNE

Language:PythonLicense:BSD-3-ClauseStargazers:0Issues:0Issues:0

Pneumonia-Diagnosis-using-XRays-96-percent-Recall

BEST SCORE ON KAGGLE SO FAR , EVEN BETTER THAN THE KAGGLE TEAM MEMBER WHO DID BEST SO FAR. The project is about diagnosing pneumonia from XRay images of lungs of a person using self laid convolutional neural network and tranfer learning via inceptionV3. The images were of size greater than 1000 pixels per dimension and the total dataset was tagged large and had a space of 1GB+ . My work includes self laid neural network which was repeatedly tuned for one of the best hyperparameters and used variety of utility function of keras like callbacks for learning rate and checkpointing. Could have augmented the image data for even better modelling but was short of RAM on kaggle kernel. Other metrics like precision , recall and f1 score using confusion matrix were taken off special care. The other part included a brief introduction of transfer learning via InceptionV3 and was tuned entirely rather than partially after loading the inceptionv3 weights for the maximum achieved accuracy on kaggle till date. This achieved even a higher precision than before.

Language:Jupyter NotebookLicense:MITStargazers:0Issues:0Issues:0