{
  "id": 453685,
  "title": "382nd Place Solution for the RSNA 2023 Abdominal Trauma Detection",
  "url": "/competitions/rsna-2023-abdominal-trauma-detection/discussion/453685",
  "author_name": "",
  "post_date": "2023-11-07T11:12:42.315000",
  "votes": 6,
  "comment_count": 0,
  "views": 0,
  "content": "<h1>1. Introduction</h1>\n<p>It brings me joy to be a part of the RSNA 2023 Abdominal Trauma Detection. I would like to express my gratitude to the organizers, sponsors, and Kaggle staff for their efforts, and I wish all the participants the best. I gain a lot of knowledge from this competition and other participants.</p>\n<p>I want to express my gratitude to MIRENA ANGELOVA for providing the public notebook <a href=\"https://www.kaggle.com/code/mirenaborisova/rsna-0-66-lb\" target=\"_blank\">https://www.kaggle.com/code/mirenaborisova/rsna-0-66-lb</a> </p>\n<h1>2. Context</h1>\n<ul>\n<li>Business context: <a href=\"https://www.kaggle.com/competitions/rsna-2023-abdominal-trauma-detection/overview\" target=\"_blank\">https://www.kaggle.com/competitions/rsna-2023-abdominal-trauma-detection/overview</a></li>\n<li>Data context: <a href=\"https://www.kaggle.com/competitions/rsna-2023-abdominal-trauma-detection/data\" target=\"_blank\">https://www.kaggle.com/competitions/rsna-2023-abdominal-trauma-detection/data</a></li>\n</ul>\n<h1>3. Overview of the approach</h1>\n<p>The solution was a copy of the public notebook(Public/Private LB of 0.66708/0.67282) <br>\nwith a change in the mean multiple coefficient for extravasation injuries and any injuries to 27 (Public/Private LB of 0.66669/0.67180).</p>\n<p>The data preprocessing process is not used.<br>\nThe algorithms employed are Mean (without a model).</p>\n<ul>\n<li>Algorithm and Inference:  <a href=\"url\" target=\"_blank\">https://www.kaggle.com/code/liudacheldieva/rsna-0-66-lb-aa7716?scriptVersionId=141544801</a>  <br>\n( copy from codes provided by MIRENA ANGELOVA in a public notebook  <a href=\"url\" target=\"_blank\">https://www.kaggle.com/code/mirenaborisova/rsna-0-66-lb?scriptVersionId=141091151</a>)<br>\nThe validation strategy has not been implemented.</li>\n</ul>\n<table>\n<thead>\n<tr>\n<th>mean()</th>\n<th>public notebook Mean multiple by</th>\n<th>my Mean multiple by</th>\n</tr>\n</thead>\n<tbody>\n<tr>\n<td>bowel_healthy</td>\n<td></td>\n<td></td>\n</tr>\n<tr>\n<td>bowel_<strong>injury</strong></td>\n<td>4</td>\n<td>-</td>\n</tr>\n<tr>\n<td>extravasation_healthy</td>\n<td></td>\n<td></td>\n</tr>\n<tr>\n<td>extravasation_<strong>injury</strong></td>\n<td>28</td>\n<td>27</td>\n</tr>\n<tr>\n<td>kidney_healthy</td>\n<td></td>\n<td></td>\n</tr>\n<tr>\n<td>kidney_<strong>low</strong></td>\n<td>4</td>\n<td>-</td>\n</tr>\n<tr>\n<td>kidney_<strong>high</strong></td>\n<td>6</td>\n<td>-</td>\n</tr>\n<tr>\n<td>liver_healthy</td>\n<td></td>\n<td></td>\n</tr>\n<tr>\n<td>liver_<strong>low</strong></td>\n<td>4</td>\n<td>-</td>\n</tr>\n<tr>\n<td>liver_<strong>high</strong></td>\n<td>6</td>\n<td>-</td>\n</tr>\n<tr>\n<td>spleen_healthy</td>\n<td></td>\n<td></td>\n</tr>\n<tr>\n<td>spleen_<strong>low</strong></td>\n<td>4</td>\n<td>-</td>\n</tr>\n<tr>\n<td>spleen_<strong>high</strong></td>\n<td>6</td>\n<td>-</td>\n</tr>\n<tr>\n<td>any_<strong>injury</strong></td>\n<td>28</td>\n<td>27</td>\n</tr>\n</tbody>\n</table>\n<p>This means from the training set used to fill in the solution values.</p>\n<h1>4. Method</h1>\n<p>Solution: Mean.</p>\n<table>\n<thead>\n<tr>\n<th>notebook</th>\n<th>score(private)</th>\n</tr>\n</thead>\n<tbody>\n<tr>\n<td>MIRENA ANGELOVA public <a href=\"https://www.kaggle.com/code/mirenaborisova/rsna-0-66-lb\" target=\"_blank\">https://www.kaggle.com/code/mirenaborisova/rsna-0-66-lb</a></td>\n<td>0.67282</td>\n</tr>\n<tr>\n<td>Change   feature.split('_')[1] == 'injury' and feature != 'bowel_injury':   submission[feature] *= 28 <br> to   submission[feature] *= 27</td>\n<td>0.6718</td>\n</tr>\n</tbody>\n</table>\n<p><br></p>\n<h1>5. Final result</h1>\n<table>\n<thead>\n<tr>\n<th>low or bowel_injury</th>\n<th>high</th>\n<th>linjury and   not eq bowel_injury</th>\n<th>public LB</th>\n<th>private LB</th>\n</tr>\n</thead>\n<tbody>\n<tr>\n<td>mean * 4</td>\n<td>mean * 6</td>\n<td>mean * 27</td>\n<td><strong>0.6718</strong></td>\n<td>0.66669</td>\n</tr>\n<tr>\n<td>mean * 4</td>\n<td>mean * 6</td>\n<td>mean * 29</td>\n<td>0.6739</td>\n<td>0.66754</td>\n</tr>\n<tr>\n<td>mean * 4</td>\n<td>mean * 5</td>\n<td>mean * 28</td>\n<td>0.67335</td>\n<td>0.66502</td>\n</tr>\n<tr>\n<td>mean * 3</td>\n<td>mean * 6</td>\n<td>mean * 28</td>\n<td>0.67181</td>\n<td>0.66819</td>\n</tr>\n<tr>\n<td>mean * 4</td>\n<td>mean * 6</td>\n<td>mean * 28</td>\n<td>0.67282</td>\n<td>0.66708</td>\n</tr>\n</tbody>\n</table>\n<h1>6. Sources</h1>\n<ul>\n<li><a href=\"url\" target=\"_blank\">https://www.kaggle.com/code/mirenaborisova/rsna-0-66-lb?scriptVersionId=141091151</a></li>\n</ul>",
  "messages": [
    {
      "id": 2515934,
      "postDate": "2023-11-07T11:12:42.317Z",
      "content": "<h1>1. Introduction</h1>\n<p>It brings me joy to be a part of the RSNA 2023 Abdominal Trauma Detection. I would like to express my gratitude to the organizers, sponsors, and Kaggle staff for their efforts, and I wish all the participants the best. I gain a lot of knowledge from this competition and other participants.</p>\n<p>I want to express my gratitude to MIRENA ANGELOVA for providing the public notebook <a href=\"https://www.kaggle.com/code/mirenaborisova/rsna-0-66-lb\" target=\"_blank\">https://www.kaggle.com/code/mirenaborisova/rsna-0-66-lb</a> </p>\n<h1>2. Context</h1>\n<ul>\n<li>Business context: <a href=\"https://www.kaggle.com/competitions/rsna-2023-abdominal-trauma-detection/overview\" target=\"_blank\">https://www.kaggle.com/competitions/rsna-2023-abdominal-trauma-detection/overview</a></li>\n<li>Data context: <a href=\"https://www.kaggle.com/competitions/rsna-2023-abdominal-trauma-detection/data\" target=\"_blank\">https://www.kaggle.com/competitions/rsna-2023-abdominal-trauma-detection/data</a></li>\n</ul>\n<h1>3. Overview of the approach</h1>\n<p>The solution was a copy of the public notebook(Public/Private LB of 0.66708/0.67282) <br>\nwith a change in the mean multiple coefficient for extravasation injuries and any injuries to 27 (Public/Private LB of 0.66669/0.67180).</p>\n<p>The data preprocessing process is not used.<br>\nThe algorithms employed are Mean (without a model).</p>\n<ul>\n<li>Algorithm and Inference:  <a href=\"url\" target=\"_blank\">https://www.kaggle.com/code/liudacheldieva/rsna-0-66-lb-aa7716?scriptVersionId=141544801</a>  <br>\n( copy from codes provided by MIRENA ANGELOVA in a public notebook  <a href=\"url\" target=\"_blank\">https://www.kaggle.com/code/mirenaborisova/rsna-0-66-lb?scriptVersionId=141091151</a>)<br>\nThe validation strategy has not been implemented.</li>\n</ul>\n<table>\n<thead>\n<tr>\n<th>mean()</th>\n<th>public notebook Mean multiple by</th>\n<th>my Mean multiple by</th>\n</tr>\n</thead>\n<tbody>\n<tr>\n<td>bowel_healthy</td>\n<td></td>\n<td></td>\n</tr>\n<tr>\n<td>bowel_<strong>injury</strong></td>\n<td>4</td>\n<td>-</td>\n</tr>\n<tr>\n<td>extravasation_healthy</td>\n<td></td>\n<td></td>\n</tr>\n<tr>\n<td>extravasation_<strong>injury</strong></td>\n<td>28</td>\n<td>27</td>\n</tr>\n<tr>\n<td>kidney_healthy</td>\n<td></td>\n<td></td>\n</tr>\n<tr>\n<td>kidney_<strong>low</strong></td>\n<td>4</td>\n<td>-</td>\n</tr>\n<tr>\n<td>kidney_<strong>high</strong></td>\n<td>6</td>\n<td>-</td>\n</tr>\n<tr>\n<td>liver_healthy</td>\n<td></td>\n<td></td>\n</tr>\n<tr>\n<td>liver_<strong>low</strong></td>\n<td>4</td>\n<td>-</td>\n</tr>\n<tr>\n<td>liver_<strong>high</strong></td>\n<td>6</td>\n<td>-</td>\n</tr>\n<tr>\n<td>spleen_healthy</td>\n<td></td>\n<td></td>\n</tr>\n<tr>\n<td>spleen_<strong>low</strong></td>\n<td>4</td>\n<td>-</td>\n</tr>\n<tr>\n<td>spleen_<strong>high</strong></td>\n<td>6</td>\n<td>-</td>\n</tr>\n<tr>\n<td>any_<strong>injury</strong></td>\n<td>28</td>\n<td>27</td>\n</tr>\n</tbody>\n</table>\n<p>This means from the training set used to fill in the solution values.</p>\n<h1>4. Method</h1>\n<p>Solution: Mean.</p>\n<table>\n<thead>\n<tr>\n<th>notebook</th>\n<th>score(private)</th>\n</tr>\n</thead>\n<tbody>\n<tr>\n<td>MIRENA ANGELOVA public <a href=\"https://www.kaggle.com/code/mirenaborisova/rsna-0-66-lb\" target=\"_blank\">https://www.kaggle.com/code/mirenaborisova/rsna-0-66-lb</a></td>\n<td>0.67282</td>\n</tr>\n<tr>\n<td>Change   feature.split('_')[1] == 'injury' and feature != 'bowel_injury':   submission[feature] *= 28 <br> to   submission[feature] *= 27</td>\n<td>0.6718</td>\n</tr>\n</tbody>\n</table>\n<p><br></p>\n<h1>5. Final result</h1>\n<table>\n<thead>\n<tr>\n<th>low or bowel_injury</th>\n<th>high</th>\n<th>linjury and   not eq bowel_injury</th>\n<th>public LB</th>\n<th>private LB</th>\n</tr>\n</thead>\n<tbody>\n<tr>\n<td>mean * 4</td>\n<td>mean * 6</td>\n<td>mean * 27</td>\n<td><strong>0.6718</strong></td>\n<td>0.66669</td>\n</tr>\n<tr>\n<td>mean * 4</td>\n<td>mean * 6</td>\n<td>mean * 29</td>\n<td>0.6739</td>\n<td>0.66754</td>\n</tr>\n<tr>\n<td>mean * 4</td>\n<td>mean * 5</td>\n<td>mean * 28</td>\n<td>0.67335</td>\n<td>0.66502</td>\n</tr>\n<tr>\n<td>mean * 3</td>\n<td>mean * 6</td>\n<td>mean * 28</td>\n<td>0.67181</td>\n<td>0.66819</td>\n</tr>\n<tr>\n<td>mean * 4</td>\n<td>mean * 6</td>\n<td>mean * 28</td>\n<td>0.67282</td>\n<td>0.66708</td>\n</tr>\n</tbody>\n</table>\n<h1>6. Sources</h1>\n<ul>\n<li><a href=\"url\" target=\"_blank\">https://www.kaggle.com/code/mirenaborisova/rsna-0-66-lb?scriptVersionId=141091151</a></li>\n</ul>",
      "rawMarkdown": "# 1. Introduction\n\nIt brings me joy to be a part of the RSNA 2023 Abdominal Trauma Detection. I would like to express my gratitude to the organizers, sponsors, and Kaggle staff for their efforts, and I wish all the participants the best. I gain a lot of knowledge from this competition and other participants.\n\nI want to express my gratitude to MIRENA ANGELOVA for providing the public notebook https://www.kaggle.com/code/mirenaborisova/rsna-0-66-lb \n\n# 2. Context\n\n- Business context: https://www.kaggle.com/competitions/rsna-2023-abdominal-trauma-detection/overview\n- Data context: https://www.kaggle.com/competitions/rsna-2023-abdominal-trauma-detection/data\n\n# 3. Overview of the approach\n\nThe solution was a copy of the public notebook(Public/Private LB of 0.66708/0.67282) \nwith a change in the mean multiple coefficient for extravasation injuries and any injuries to 27 (Public/Private LB of 0.66669/0.67180).\n \nThe data preprocessing process is not used.\nThe algorithms employed are Mean (without a model).\n- Algorithm and Inference:  [https://www.kaggle.com/code/liudacheldieva/rsna-0-66-lb-aa7716?scriptVersionId=141544801](url)  \n( copy from codes provided by MIRENA ANGELOVA in a public notebook  [https://www.kaggle.com/code/mirenaborisova/rsna-0-66-lb?scriptVersionId=141091151](url))\nThe validation strategy has not been implemented.\n\n| mean() | public notebook Mean multiple by| my Mean multiple by|\n| --- | --- | --- |\n|  bowel_healthy | | |\n| bowel_**injury**| 4|-|\n| extravasation_healthy| ||\n| extravasation_**injury**|28 |27 |\n| kidney_healthy| ||\n| kidney_**low**|4|-|\n| kidney_**high**|6 |- |\n| liver_healthy| | |\n| liver_**low**| 4|-|\n| liver_**high**| 6|-|\n| spleen_healthy| | |\n| spleen_**low**| 4|-|\n| spleen_**high**| 6|-|\n| any_**injury**| 28|27|\n\n\n\nThis means from the training set used to fill in the solution values.\n\n# 4. Method \n\nSolution: Mean.\n\n| notebook |  score(private) | \n| --- | --- |\n| MIRENA ANGELOVA public https://www.kaggle.com/code/mirenaborisova/rsna-0-66-lb | 0.67282 | \n| Change   feature.split('_')[1] == 'injury' and feature != 'bowel_injury':   submission[feature] *= 28 <br> to   submission[feature] *= 27 |  0.6718  |\n\n<br>\n# 5. Final result\n\n| low or bowel_injury | high | linjury and   not eq bowel_injury |  public LB | private LB |\n| --- | --- | --- | --- | --- |\n| mean * 4  |  mean * 6 | mean * 27  | **0.6718**     | 0.66669 |\n| mean * 4  |  mean * 6 | mean * 29  | 0.6739    | 0.66754 |\n| mean * 4  |  mean * 5 | mean * 28  |  0.67335 | 0.66502 |\n| mean * 3  |  mean * 6 | mean * 28  | 0.67181   | 0.66819 |\n| mean * 4  |  mean * 6 |  mean * 28 |  0.67282 | 0.66708 | \n\n# 6. Sources\n- [https://www.kaggle.com/code/mirenaborisova/rsna-0-66-lb?scriptVersionId=141091151](url)",
      "votes": 6
    }
  ],
  "comments": [],
  "raw_markdown_by_id": {
    "2515934": "# 1. Introduction\n\nIt brings me joy to be a part of the RSNA 2023 Abdominal Trauma Detection. I would like to express my gratitude to the organizers, sponsors, and Kaggle staff for their efforts, and I wish all the participants the best. I gain a lot of knowledge from this competition and other participants.\n\nI want to express my gratitude to MIRENA ANGELOVA for providing the public notebook https://www.kaggle.com/code/mirenaborisova/rsna-0-66-lb \n\n# 2. Context\n\n- Business context: https://www.kaggle.com/competitions/rsna-2023-abdominal-trauma-detection/overview\n- Data context: https://www.kaggle.com/competitions/rsna-2023-abdominal-trauma-detection/data\n\n# 3. Overview of the approach\n\nThe solution was a copy of the public notebook(Public/Private LB of 0.66708/0.67282) \nwith a change in the mean multiple coefficient for extravasation injuries and any injuries to 27 (Public/Private LB of 0.66669/0.67180).\n \nThe data preprocessing process is not used.\nThe algorithms employed are Mean (without a model).\n- Algorithm and Inference:  [https://www.kaggle.com/code/liudacheldieva/rsna-0-66-lb-aa7716?scriptVersionId=141544801](url)  \n( copy from codes provided by MIRENA ANGELOVA in a public notebook  [https://www.kaggle.com/code/mirenaborisova/rsna-0-66-lb?scriptVersionId=141091151](url))\nThe validation strategy has not been implemented.\n\n| mean() | public notebook Mean multiple by| my Mean multiple by|\n| --- | --- | --- |\n|  bowel_healthy | | |\n| bowel_**injury**| 4|-|\n| extravasation_healthy| ||\n| extravasation_**injury**|28 |27 |\n| kidney_healthy| ||\n| kidney_**low**|4|-|\n| kidney_**high**|6 |- |\n| liver_healthy| | |\n| liver_**low**| 4|-|\n| liver_**high**| 6|-|\n| spleen_healthy| | |\n| spleen_**low**| 4|-|\n| spleen_**high**| 6|-|\n| any_**injury**| 28|27|\n\n\n\nThis means from the training set used to fill in the solution values.\n\n# 4. Method \n\nSolution: Mean.\n\n| notebook |  score(private) | \n| --- | --- |\n| MIRENA ANGELOVA public https://www.kaggle.com/code/mirenaborisova/rsna-0-66-lb | 0.67282 | \n| Change   feature.split('_')[1] == 'injury' and feature != 'bowel_injury':   submission[feature] *= 28 <br> to   submission[feature] *= 27 |  0.6718  |\n\n<br>\n# 5. Final result\n\n| low or bowel_injury | high | linjury and   not eq bowel_injury |  public LB | private LB |\n| --- | --- | --- | --- | --- |\n| mean * 4  |  mean * 6 | mean * 27  | **0.6718**     | 0.66669 |\n| mean * 4  |  mean * 6 | mean * 29  | 0.6739    | 0.66754 |\n| mean * 4  |  mean * 5 | mean * 28  |  0.67335 | 0.66502 |\n| mean * 3  |  mean * 6 | mean * 28  | 0.67181   | 0.66819 |\n| mean * 4  |  mean * 6 |  mean * 28 |  0.67282 | 0.66708 | \n\n# 6. Sources\n- [https://www.kaggle.com/code/mirenaborisova/rsna-0-66-lb?scriptVersionId=141091151](url)"
  }
}