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This is Our approch for the kaggle Competition for OSIC Pulmonary Fibrosis Progression

License: GNU General Public License v3.0

Jupyter Notebook 100.00%
kaggle-competition mahcine-learning image-processing

osic-pulmonary-fibrosis-progression's Introduction

OSIC-Pulmonary-Fibrosis-Progression

Basic Overview

This is Our approch for the kaggle Competition for OSIC Pulmonary Fibrosis Progression

Team Member

Ankit Sinha & Ranjeet Dhumal

The Link to the Compitition : https://www.kaggle.com/c/osic-pulmonary-fibrosis-progression/overview/description


Deadline

30th September 2020


Description

Imagine one day, your breathing became consistently labored and shallow. Months later you were finally diagnosed with pulmonary fibrosis, a disorder with no known cause and no known cure, created by scarring of the lungs. If that happened to you, you would want to know your prognosis. That’s where a troubling disease becomes frightening for the patient: outcomes can range from long-term stability to rapid deterioration, but doctors aren’t easily able to tell where an individual may fall on that spectrum. Your help, and data science, may be able to aid in this prediction, which would dramatically help both patients and clinicians.

Code Requirements

Submissions to this competition must be made through Notebooks. In order for the "Submit to Competition" button to be active after a commit, the following conditions must be met:

  • CPU Notebook <= 9 hours run-time
  • GPU Notebook <= 4 hours run-time
  • TPUs will not be available for making submissions to this competition. You are still welcome to use them for training models.
  • No internet access enabled
  • External data, freely & publicly available, is allowed. This includes pre-trained models.
  • Submission file must be named submission.csv

Dataset

The dataset is of 22.3 GB containing the following items :

Test images
Train images
Sample_submission.csv
test.csv
train.csv

Link to the dataset : https://www.kaggle.com/c/osic-pulmonary-fibrosis-progression/data

FVC (Forced Vital Capacity)

FVC measurement shows the amount of air a person can forcefully and quickly exhale after taking a deep breath. It is defined as the recorded lung capacity in ml under the Data tab. The change in FVC over the course of weeks is used for predicting the patients' lung function decline.

Even though the FVC predictions smaller than 1000 are clipped, the minimum value in training set is 827. The maximum FVC value in training set is 6399. The distribution is heavily tailed on the right end because some patients have extremely high FVC measurements. However, most of the patients are close to mean FVC.

Rules of the competition

Follow the Guide line provided by the organizer of the competition https://www.kaggle.com/c/osic-pulmonary-fibrosis-progression/rules

About the Organizer

COMPETITION TITLE: OSIC Pulmonary Fibrosis Progression

COMPETITION SPONSOR: Open Source Imaging Consortium (OSIC)

COMPETITION SPONSOR ADDRESS: 482 Century Lane, Suite 40, Holland, MI 49423

COMPETITION WEBSITE: https://www.kaggle.com/c/osic-pulmonary-fibrosis-progression

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osic-pulmonary-fibrosis-progression's Issues

Decide a Approach

We will Discuss the different approach which we can go through for this problem in this issue

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