(Mini-challenge) - Data Science Africa 2019

Organized by herilalaina - Current server time: Oct. 27, 2020, 11:35 a.m. UTC

Previous

Development Phase
June 4, 2019, 6:40 a.m. UTC

Current

Final Phase
June 7, 2019, 2 p.m. UTC

End

Competition Ends
Never

Challenge on Microscopy dataset

Brought to you by Data Science Africa committee

Source: http://air.ug/microscopy/

Presentation

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.

Our challenge

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.

Illustration

The goal of the challenge

The goal of the challenge is to build a machine learning model that can classify if a patch extracted from the original image contains parasites or not. We can see it as an binary classification problem. The following image illustrates possible positive and negative patches.

Illustration

How to participate

See: https://github.com/herilalaina/dsa_materials_challenge/tree/master/2_participating_challenge

Evaluation

You need to implement a machine learning model in order to classify (Binary classification problem) whether a patch is postive or negative.
There are 2 phases:

  • Phase 1: development phase. We provide you with labeled training data and unlabeled validation and test data. Make predictions for both datasets. However, you will receive feed-back on your performance on the validation set only. The performance of your LAST submission will be displayed on the leaderboard.
  • Phase 2: final phase. You do not need to do anything. Your last submission of phase 1 will be automatically forwarded. Your performance on the test set will appear on the leaderboard when the organizers finish checking the submissions.

This sample competition allows you to submit either:

  • Only prediction results (no code).
  • A prediction model that must be trained and tested.

The submissions are evaluated using the AUC ROC metric.

Rules

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.

Development Phase

Start: June 4, 2019, 6:40 a.m.

Description: Development phase: tune and submit your model. Scores are based on the validation set

Final Phase

Start: June 7, 2019, 2 p.m.

Description: Final phase (no submission, your last submission from the previous phase is automatically forwarded). Scores are based on test set.

Competition Ends

Never

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# Username Score
1 Dee 0.9900
2 hena_dsa2019_us 0.9882
3 afifasil 0.9857