During this period of the extremely contagious pandemic, we are experiencing how sad it is to stay in isolation and not to connect with each other in physical space as normal.
Conferences such as F8, GDC, GTC, and many others have been canceled. A deep sense of sadness and detachment for our social life is affecting most of us, and at the same time as the COVID-19, I would like to share my unwieldy personal experience.
I found a way to check if there is something inside my head and further explore my Neural Network.
It is nice to see that Facebook AI on the 12th they released FastMRI. And the 17th I received my results, the Validation Set. Unfortunately, It took almost 2 months before receiving the results, and it was such a nerve-racking situation. I hope with this implementation people won’t pass through our suffering as we did.
DICOM/DatCard to see MRI. Thanks to FastAI and Jeremy Howard I was able to analyze my analysis in a more accurate way. “At medical start-up Enlitic, Jeremy Howard led a team that used just 1,000 examples of lung CT scans with cancer to build an algorithm that was more accurate at diagnosing lung cancer than a panel of 4 expert radiologists.”
The fastMRI initiative aims to make scans up to 10 times faster than they are today, thereby improving the patient experience and making MRI scans less expensive and more accessible. Jeremy taught us how to classify 37 images of pets and I applied transfer-learning with this Fine-Grained Classification for Brain diseases.
conda install pyarrow
pip install pydicom kornia opencv-python scikit-image
fnames = get_image_files(path_img)
dls = ImageDataLoaders.from_name_re(path, fnames, pat=r'(.+)_\d+.jpg$', item_tfms=Resize(460), bs=bs, batch_tfms=[*aug_transforms(size=224, min_scale=0.75), Normalize.from_stats(*imagenet_stats)])
Trained with ResNet34. “We used a CNN backbone and a fully connected head with a single hidden layer as a classifier. ” This Neural Network has already trained with 1.5M pictures of different pictures using ImageNET in this way we can apply successfully transfer-learning.
learn = cnn_learner(dls, resnet34, metrics=error_rate).to_fp16()
Results regarding my brain situation will be published soon (in the positive case)… on my code-blog,
Pandemic: “From Ebola to COVID-19″
We tried to raise awareness back in 2015, and also Bill Gates in 2019.
I strongly suggest having a look at this game as a simulation for understanding possible outcomes and consequences:
This project is called: Aesculapius. It was designed for a pandemic in general but it used Ebola as an example. The design solution focused on the importance of raising awareness about this pandemic and emergency in order to act in a pro-active way. The real information and news need to occur immediately, in real-time, in order to eradicate the disease.
At the moment, for coronavirus, a lot of data has been gathered.
Factors that few people took into consideration:
- Source of Data: Private Hospitals don’t provide or release their data as other public institutions such as Italy.
- Healthcare system structure, people in Italy have the Health service free and as soon as they feel sick they directly go there.
- People in the USA have a flu vaccine every year.
- Culture: Italy is immersed in Piazzas, social spaces that embrace the culture and spirit of Italians, this facilitated the spread of the diseased.
We should always place ethical values first and operate in a PRO-ACTIVE way with dedication and love. Take care of yourself, at this time #STAYHOME #LOVEYOURFAMILY #LEARN and improve your community and society.