Although microscopes are common in Uganda and other developing countries, a shortage of lab technicians to operate them means that access to quality diagnostic services is limited for much of the population. This leads to misdiagnoses of disease, which in turn causes life-threatening conditions to be incorrectly treated, drug resistance, and the economic burden of buying unecessary drugs. Even where health facilities have lab technicians, they are often oversubscribed and have difficulty spending enough time on each sample to give a confident diagnosis. Given that smartphones are widely owned across the developing world, there is a technological opportunity to address this problem: phones can be used to capture and process microscopy images. This project aims to produce a functioning point-of-care diagnosis system on this principle, capable of running on multiple microscope and phone combinations. Our work exploits recent technological advances in 3D printing and deep learning to produce effective hardware and software respectively.
The goal is to train machine learning methods to recognise different pathogen objects, and to make this accessible in the form of an Android application usable at the point of care. This work began with machine learning methods based on extracting statistical characterisations of the shapes in each image.
You need to download the starting kit.
Install Anaconda Python 3.6.6, Tensorflow (2.0.0), opencv-python (4.0.1), scikit-image (0.15.0)
run your code within the Codalab docker (inside the docker, python 3.6 is called python3):
Usage of the starting kit:
For submission, you can either submit code or prediction files.
The goal is to count the number of parasites on each image. We divide the whole process into two step pipeline.
There are 2 phases:
This sample competition allows you to submit either:
The submissions are evaluated using the AUC ROC metric (step 1) and Mean Squared Error for (step 2).
Submissions must be made before the end of phase 1. You may submit 5 submissions every day and 100 in total.
This challenge is governed by the general ChaLearn contest rules.
Start: May 1, 2019, midnight
Description: Development phase: tune your models and submit prediction results, trained model, or untrained model.
Start: June 5, 2019, midnight
Description: Final phase
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