## Inspiration

With most of our teammates passionate in research and academia, (Biotech, Astrophysics, Physics) we all saw a common issue amongst these fields being that traditional imaging techniques often impair the quality of research. This drove us to build SCALR, which is a deep learning instrument designed to improve image resolution while staying true to the content within the frame.

## What it does

SCALR is a medical image processing tool aiming to improve resolution in cellular imaging by leveraging a generator autoencoder trained against a discriminator in order to generate highly detailed and sharp images with 16x the pixel density (4x the width and height). The target demographic is to help researchers in the drug discovery industry to better image cellular structures. This is done by extending an architecture developed by Nvidia, called SRRES-NET [<https://arxiv.org/pdf/1609.04802.pdf>], by turning the RES-NET into an autoencoder to force the model to encode high dimensionality features allowing for sharp image reconstruction.

## How we built it

We began the processes by building the Super-Res Generator by prototyping the model in Python Notebook. We then migrated the prototype model into a GCP VM to train the very large model on a far larger dataset. Then the model was fined tuned, saved and integrated into a flask app to serve the model's features to potential clients displayed using ReactJS. This entire process involved various full-stack web dev and machine learning skills.

## Challenges we ran into

During our process, we struggled to configure and maintain a python environment across all 3 systems causing major bottlenecks in our production cycle. We also struggled to integrate and connect react with the Flask application as the React sent files unreadable by flask. Despite these bottlenecks, we eventually overcame said challenges and delivered a polished app.

## Accomplishments that we're proud of

We're proud that we brainstormed a novel method in training GAN's specifically relating to medical images. This technique involved training the generator against targets then injecting that model GAN which is trained with the feedback from the discriminator. This helped produce images with more clarity and sharpness with our overall results.

## What we learned

We learned how important it is to maintain a clean, and well-documented codebase. None of us had a lot of experience collaborating on code, and so we ended up wasting some time trying to understand each others' code and how to make it work together. This took up some extra time but we think the learning experience was totally worth it.

## What's next for Scalr

We would love to talk to companies and researchers working on cell-profiling or other biomedical images such as Novartis. To our knowledge, the architecture we developed is unique and we're sure people who are really knowledgeable in the field (ML & cell-profiling, for example) could give us a ton of advice on how to improve it.
## Inspiration

Energy is the foundation for everyday living. Productivity from the workplace to lifestyle—sleep, nutrition, fitness, social interactions—are dependant on sufficient energy levels required for each activity [1]. Various generalized interventions have been proposed to address energy levels, but currently no method has proposed a personal approach using daily schedules/habits as determinants for energy.

## What it does

Boost AI is an iOS application that uses machine learning to predict energy levels based on daily habits. Simple and user-specific questions on sleep schedule, diet, fitness, social interaction, and current energy level will be used as determinants to predict future energy level. Notifications will give the user personalized recommendations to increase energy throughout the day.

Boost AI allows you to visualize your energy trends over time, including predictions for personalized intervention based on your own lifestyle.

## How we built it

We used MATLAB and TensorFlow for our machine learning framework. The current backend utilizes a support vector machine that is trained on simulated data, based on a subject's "typical" week, with relevant data-augmentation. The linear support vector machine is continually trained with each new user input, and each prediction is based on a moving window, as well as historical daily trends. We have further trained an artificial neural network to make these same predictions, using tensorflow with a keras wrapper. In the future this neural network model will be used to allow for an individual to get accurate predictions with their first use by applying a network trained on a large and diverse set of individuals, then continually fine tuning their personal network to have the best predictions and accurate trends for them. We used Sketch to visualize our iOS application prototype.

## Challenges we ran into

Although we come from the healthcare field, we were limited in domain knowledge in human energy and productivity. We did research on each parameter that is determinant to energy levels.

## Accomplishments that we're proud of

Boost AI is strongly translatable to improving energy in everyday life. We’re proud of the difference it can make to the every day lives of our users.

## What's next for Boost AI

We aim to improve our prototype by training our framework with a real world dataset. We would like to explore two main applications:

**1) Workspace.** Boost AI can be optimized in the workplace by implementing the application into workspace specific softwares. We predict that Boost AI will "boost" energy with specific individual interventions for improved productivity and output.

**2) Healthcare.** Boost AI can be use health based data such as biometric markers and researched questionnaires to predict energy. The data and trends can be used for clinical-driven, intervention and improvements, as well as personal use.

## References:

[1] Arnetz, BB., Broadbridge, CL., Ghosh, S. (2014) Longitudinal determinants of energy levels in knowledge workers. Journal of Occupational Environmental Medicine.
## Inspiration

Increasing amounts of COVID-19 related data, so consider the following: How might we apply existing technologies and systems to best use existing COVID-19 data?

## What it does

With an x-ray image of an individual's healthy lungs, we are able to use machine learning techniques to add features indicative of COVID-19. Given that we're handling an image, we took an approach that applied the use of Generative Adversarial Networks (GANs) to create a new image of the same lungs except with added COVID-19 properties. COVID-19 may manifest in an x-ray as cloudy regions in the lungs, known as ground glass opacities.

## How we built it

We built an Auxiliary-Classifier GAN (arXiv:1610.09585) that produces lung x-rays with or without COVID-19 properties based on an inputted condition. After the generator has been trained, an inputted healthy lung x-ray is encoded into a latent vector via gradient descent with a VGG perceptual loss (arXiv:1802.05701, arXiv:1603.08155). Then, a condition vector is concatenated with this latent vector and run through the generator to produce the same lung x-ray but with COVID-19 ground glass opacities. This model was written in Pytorch. Hyperparameters and code can be found at the following GitHub. The training data included around 2000 total 64x64 COVID positive and negative x-rays.

## Challenges we ran into

Exporting the GAN model into Flask.

## Accomplishments that we're proud of

Clean UI, cool usage of GANs, and building it into a easy-to-use application.

## What we learned

Linking several different skillsets together into a cohesive final product.

## What's next for COVIDify

Continue to collect and train data sets of various lung x-rays to improve the training and classifying steps that are applied to the GAN framework. We can also apply to larger images to increase resolution, but this will require more computational power. We're also curious to see how this framework can be applied to different illnesses. So if there is a novel disease, researchers/doctors/officials may use this GAN methodology to study attributes of the illness.