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  "source_url": "https://www.kaggle.com/competitions/echo2022/data",
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      "content": "# Files\n*  **train_data/train_data/2CH** - 2CH echo loops, training data\n*  **train_data/train_data/4CH** - 4CH echo loops, training data\n*  **test_data/test_data/4CH** - 4CH echo loops, test data\n*  **test_data/test_data/2CH** - 2CH echo loops, test data\n*   **train_data.csv** - patient number and the corresponding ejection fraction\n*   **sample_submission.csv** - a sample submission file in the correct format (the numbers in LV_ef are random numbers between 0 and 100)\n\n## .npy files\nWhen importing the .npy-files using `numpy.load` the resulting arrays are structured as follow:\n\n```\n(number of images in the loop, image height, image width)\n```\n## .csv files\nThe csv files have two colums\n\n- `Patient_number` - the patient number (notice that the test patients have one more digit in the patient number than the train patients)\n- `LV_ef` - a float number with one decimal containing a number between 0 and 100 (%)",
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      "content": "# Introduction\nCardiac ejection fraction (EF) is a measurement, expressed as a percentage, of how much blood the left ventricle pumps out with each contraction [1]. Low cardiac EF might be a sign of heart failure, and therefore have clinical relevance. Cardiac EF is usually measured in the left ventricle, which is the heart's main pumping chamber. The left chamber pumps oxygen-rich blood up into your body's main artery (aorta) to the rest of the body [2]. \n\nToday cardiovascular MRI (CMR) is the gold standard for the calculation of left ventricular ejection fraction (LVEF)[3]. However, each cardiac MRI scan can cost between $1000-5000 or on average 5.5-times the price of echocardiography [4]. It would therefore have great health economical benefits if echocardiograms could be used instead of CMR to determine the left ventricular ejection fraction.\n\n## The challenge\nAI has shown great potential in medical image classification and its reason to believe that it can be beneficial in terms of interpreting echocardiograms. In this dataset, we present echo loops, or sequences of echocardiograms. In the training set, there are images from 400 patients and in the test set, there are 50 patients. The objective is to predict the cardiac EF of the 50 patients in the test set.\n\n#Data\nThe data set contains both 2 channel (2CH) echo images and 4 channel (4CH) echo images. 2CH images show only the left ventricle and the left atrium, while 4CH shows both the left and right atrium and ventricle. The competitors are free to choose whether they want to use only 4CH, only 2CH, or both in their training and prediction. \n\nThe image data are stored in .npy files. These files can be loaded into Python using `numpy.load`\n```\nimport numpy as np\n\nnp.load(\"./my_path/my_echofile.npy\")\n```\nEach .npy-file contains an entire echo loop. All images inside an echo loop have the same resolution and image size, but the image size varies from patient to patient. \n\nThe ejection fraction (ground truth) for the training data is given inside the file called **train_data.csv**.  This file can be uploaded to Python using `pandas.read_csv`\n```\nimport pandas as pd\n\npd.read_csv(\"./my_path/train_data.csv\")\n```\nAnd finally, after you have trained your model and done the predictions on the test set you can populate the lv EF column in the sample_submission.csv-file with your own predictions and submit it to this competition.\n\n![echo animation](https://raw.githubusercontent.com/Bsingstad/DL-images/main/EchoAnimation.gif)\n \n# Acknowledgements\n\nThis data set is derived from the [CAMUS data set](https://www.creatis.insa-lyon.fr/Challenge/camus/) and any scientific work using this data set should therefore cite the following source [5] \n\n# Reference\n[1] https://www.heart.org/en/health-topics/heart-failure/diagnosing-heart-failure/ejection-fraction-heart-failure-measurement\n[2] https://www.mayoclinic.org/tests-procedures/ekg/expert-answers/ejection-fraction/faq-20058286\n[3] Simpson R, Bromage D, Dancy L, et al6 Comparing echocardiography and cardiac magnetic resonance measures of ejection fraction: implications for HFMRF researchHeart 2018;104:A3.\n[4] https://www.acc.org/latest-in-cardiology/articles/2019/01/07/12/42/for-the-fits-the-future-of-cardiovascular-imaging-considerations-for-fits\n[5] S. Leclerc, E. Smistad, J. Pedrosa, A. Ostvik, et al.\n\"Deep Learning for Segmentation using an Open Large-Scale Dataset in 2D Echocardiography\" in IEEE Transactions on Medical Imaging,\nearly acces, 2019\n\n",
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      "name": "Evaluation",
      "content": "The objective in this competition is to predict the left ventricular ejection fraction. This is a number between 0 and 100 (%) and is therfore considered to be a regression task and we therefore propose a Root mean squared error to be the metric that is used to rank the entries in this competition.\n\n![RMSE](https://media.geeksforgeeks.org/wp-content/uploads/20200622171741/RMSE1.jpg)\n\n\n## Submission Format\n\nWe recommend you to use the provided submission template (sample_submission.csv). This ensures that you have the right format. The only thing you need to do is to change the LV ef placeholder values to your predicted values.\n",
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    {
      "name": "rules",
      "content": "We only have 3 simple rules:\n\n1. Don't cheat \n2. Be nice to your fellow Kagglers\n3. Have fun",
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