{ "id": 254417, "title": "[Compilation] RSNA MICCAI Brain Tumor Radiogenomic Classification : Good Baseline solutions", "url": "/competitions/rsna-miccai-brain-tumor-radiogenomic-classification/discussion/254417", "author_name": "KritiDoneria", "post_date": "2021-07-21T17:41:24.022000", "votes": 39, "comment_count": 12, "views": 0, "content": "
    \n

  1. An approach with PyTorch EfficientNet 3D by @xuxu1234

    2. \n

  2. A beginner friendly basic EDA by @aristotle609

    3. \n

  3. A very detailed, publications heavy notebook by @mpwolke

    4. \n

  4. Brain Tumor- EDA with Animations and Modeling by GM @ihelon . This one also has a great definition for competition metric

    5. \n

  5. Basic Pytorch CNN Baseline by @furcifer

    6. \n

  6. A great notebook treating sequential image as video by @ayuraj

    7. \n

  7. Using pre-trained models for baseline by @mrinath . This is still work in progress.

    8. \n

  8. Image augmentation by @furcifer

    9. \n

  9. A no nonsense notebook for non-beginners by @hamzajebbar

    10. \n

  10. Effnet 3D from 1-D MRI by @rluethy

    11. \n
\n

A huge shoutout to all these people who risk their LB positions in the spirit of community.
\nPlease upvote their notebooks if you find it useful.
\nThanks

", "messages": [ { "id": 1396020, "postDate": "2021-07-21T17:41:24.023Z", "content": "
    \n

  1. An approach with PyTorch EfficientNet 3D by @xuxu1234

    2. \n

  2. A beginner friendly basic EDA by @aristotle609

    3. \n

  3. A very detailed, publications heavy notebook by @mpwolke

    4. \n

  4. Brain Tumor- EDA with Animations and Modeling by GM @ihelon . This one also has a great definition for competition metric

    5. \n

  5. Basic Pytorch CNN Baseline by @furcifer

    6. \n

  6. A great notebook treating sequential image as video by @ayuraj

    7. \n

  7. Using pre-trained models for baseline by @mrinath . This is still work in progress.

    8. \n

  8. Image augmentation by @furcifer

    9. \n

  9. A no nonsense notebook for non-beginners by @hamzajebbar

    10. \n

  10. Effnet 3D from 1-D MRI by @rluethy

    11. \n
\n

A huge shoutout to all these people who risk their LB positions in the spirit of community.
\nPlease upvote their notebooks if you find it useful.
\nThanks

", "rawMarkdown": "1. [An approach with PyTorch EfficientNet 3D](https://www.kaggle.com/xuxu1234/efficientnet3d-for-mri) by @xuxu1234 \n\n2. [A beginner friendly basic EDA](https://www.kaggle.com/aristotle609/basic-eda-rsna) by @aristotle609\n\n3. [A very detailed, publications heavy notebook](https://www.kaggle.com/mpwolke/glioblastoma-mri-dicom-format) by @mpwolke\n\n4. [Brain Tumor- EDA with Animations and Modeling](https://www.kaggle.com/ihelon/brain-tumor-eda-with-animations-and-modeling) by GM @ihelon . This one also has a great definition for competition metric\n\n5. [Basic Pytorch CNN Baseline](https://www.kaggle.com/furcifer/no-baseline-pytorch-cnn-for-mri) by @furcifer \n\n6. [A great notebook treating sequential image as video](https://www.kaggle.com/ayuraj/train-brain-tumor-as-video-classification-w-b) by @ayuraj\n\n7. [Using pre-trained models for baseline](https://www.kaggle.com/mrinath/using-pretrained-models-for-3d-data) by @mrinath . This is still work in progress.\n\n8. [Image augmentation](https://www.kaggle.com/furcifer/mri-data-augmentation-pipeline) by @furcifer\n\n9. [A no nonsense notebook for non-beginners](https://www.kaggle.com/hamzajebbar/brain-tumor-radiogenomic-classification) by @hamzajebbar\n\n10. [Effnet 3D from 1-D MRI](https://www.kaggle.com/rluethy/efficientnet3d-with-one-mri-type) by @rluethy\n\n\nA huge shoutout to all these people who risk their LB positions in the spirit of community.\nPlease upvote their notebooks if you find it useful.\nThanks", "votes": 39 }, { "id": 1396822, "postDate": "2021-07-22T14:13:07.830Z", "content": "

I want to add some of my \"favorites'\" :

\n

All Images/Dicom collection from David Roberts in this competition: https://www.kaggle.com/davidbroberts till now 4 in RSNA/MICCAI and many in SIIM FISABIO. For those that wants to learn dcm files his work is a great source.

\n

By YeongHyeon ( Convert DICOM to Numpy Array (Super Simple).
\nBeautiful code, simple for him (not for me yet).
\nhttps://www.kaggle.com/yeonghyeon/convert-dicom-to-numpy-array-super-simple/comments#1391881

\n

I made some amount of research, reading articles related to this Competition. And learnt many things about MGMT, Flair, T1 and T2 weighted.
\nBut I credit all the Dicom script to Md Redwan Karim Sony and his contributions in RSNA STR Pulmonary Embolism Detection, like those below :

\n

https://www.kaggle.com/redwankarimsony/visualizing-and-analyzing-dicoms-in-python/notebook?select=train.csv
\nhttps://www.kaggle.com/redwankarimsony/ct-scans-dicom-files-windowing-explained

\n

Thank you Kriti, I'm flattered to be in your list.

", "rawMarkdown": "I want to add some of my \"favorites'\" :\n\nAll Images/Dicom collection from David Roberts in this competition: https://www.kaggle.com/davidbroberts till now 4 in RSNA/MICCAI and many in SIIM FISABIO. For those that wants to learn dcm files his work is a great source.\n\nBy YeongHyeon ( Convert DICOM to Numpy Array (Super Simple).\nBeautiful code, simple for him (not for me yet).\nhttps://www.kaggle.com/yeonghyeon/convert-dicom-to-numpy-array-super-simple/comments#1391881\n\nI made some amount of research, reading articles related to this Competition. And learnt many things about MGMT, Flair, T1 and T2 weighted.\nBut I credit all the Dicom script to Md Redwan Karim Sony and his contributions in RSNA STR Pulmonary Embolism Detection, like those below :\n\n https://www.kaggle.com/redwankarimsony/visualizing-and-analyzing-dicoms-in-python/notebook?select=train.csv \nhttps://www.kaggle.com/redwankarimsony/ct-scans-dicom-files-windowing-explained\n\nThank you Kriti, I'm flattered to be in your list.\n\n", "votes": 5, "replies": [ { "id": 1398015, "postDate": "2021-07-23T17:23:57.427Z", "content": "

I'm a fan of your awesome research skills @mpwolke

", "rawMarkdown": "I'm a fan of your awesome research skills @mpwolke ", "votes": 2 }, { "id": 1410383, "postDate": "2021-08-02T17:46:54.330Z", "rawMarkdown": "", "isDeleted": true }, { "id": 1410381, "postDate": "2021-08-02T17:46:54.330Z", "content": "

Fantastic additions! Thanks for this additional collection @mpwolke !

", "rawMarkdown": "Fantastic additions! Thanks for this additional collection @mpwolke !", "votes": 2 } ] }, { "id": 1408345, "postDate": "2021-08-02T12:27:51.243Z", "content": "

Thanks for the collection 😃 Unfortunately, you mislabeled the author for 1 and 5 🤕

", "rawMarkdown": "Thanks for the collection 😃 Unfortunately, you mislabeled the author for 1 and 5 🤕", "votes": 1, "replies": [ { "id": 1409968, "postDate": "2021-08-02T17:25:13.620Z", "content": "

Thanks for pointing it out. Corrected 5, not so sure about 1.
\nThanks @furcifer

", "rawMarkdown": "Thanks for pointing it out. Corrected 5, not so sure about 1.\nThanks @furcifer " } ] }, { "id": 1406042, "postDate": "2021-07-31T12:34:32.130Z", "content": "

whooohooo! and i agree with you, high praise to those who exemplify the spirit of the community!

", "rawMarkdown": "whooohooo! and i agree with you, high praise to those who exemplify the spirit of the community!", "votes": 1, "replies": [ { "id": 1409904, "postDate": "2021-08-02T17:22:29.130Z", "content": "

I agree @elenaeb

", "rawMarkdown": "I agree @elenaeb " } ] }, { "id": 1396489, "postDate": "2021-07-22T06:53:03.830Z", "content": "

Thanks for mentioning my notebook😊. Good luck to all those taking part in the competition.👊

", "rawMarkdown": "Thanks for mentioning my notebook😊. Good luck to all those taking part in the competition.👊", "votes": 1, "replies": [ { "id": 1398016, "postDate": "2021-07-23T17:24:26.047Z", "content": "

all the best and thanks for sharing your work @aristotle609

", "rawMarkdown": "all the best and thanks for sharing your work @aristotle609 ", "votes": 1 } ] }, { "id": 1396620, "postDate": "2021-07-22T09:43:05.107Z", "content": "

Great collection to start, thank you.

", "rawMarkdown": "Great collection to start, thank you.", "votes": 2, "replies": [ { "id": 1398017, "postDate": "2021-07-23T17:24:37.240Z", "content": "

happy to help @tharun2001

", "rawMarkdown": "happy to help @tharun2001 ", "votes": 1 } ] } ], "comments": [ { "id": 1396822, "author_name": "Marília Prata", "author_url": "", "post_date": "2021-07-22T14:13:07.830000", "content": "

I want to add some of my \"favorites'\" :

\n

All Images/Dicom collection from David Roberts in this competition: https://www.kaggle.com/davidbroberts till now 4 in RSNA/MICCAI and many in SIIM FISABIO. For those that wants to learn dcm files his work is a great source.

\n

By YeongHyeon ( Convert DICOM to Numpy Array (Super Simple).
\nBeautiful code, simple for him (not for me yet).
\nhttps://www.kaggle.com/yeonghyeon/convert-dicom-to-numpy-array-super-simple/comments#1391881

\n

I made some amount of research, reading articles related to this Competition. And learnt many things about MGMT, Flair, T1 and T2 weighted.
\nBut I credit all the Dicom script to Md Redwan Karim Sony and his contributions in RSNA STR Pulmonary Embolism Detection, like those below :

\n

https://www.kaggle.com/redwankarimsony/visualizing-and-analyzing-dicoms-in-python/notebook?select=train.csv
\nhttps://www.kaggle.com/redwankarimsony/ct-scans-dicom-files-windowing-explained

\n

Thank you Kriti, I'm flattered to be in your list.

", "votes": 5, "replies": [ { "id": 1398015, "author_name": "KritiDoneria", "author_url": "", "post_date": "2021-07-23T17:23:57.427000", "content": "

I'm a fan of your awesome research skills @mpwolke

", "votes": 2, "replies": [] }, { "id": 1410383, "author_name": "", "author_url": "", "post_date": "2021-08-02T17:46:54.330000", "content": "", "votes": 0, "replies": [] }, { "id": 1410381, "author_name": "ElenaEB", "author_url": "", "post_date": "2021-08-02T17:46:54.330000", "content": "

Fantastic additions! Thanks for this additional collection @mpwolke !

", "votes": 2, "replies": [] } ] }, { "id": 1408345, "author_name": "Zabir Al Nazi Nabil", "author_url": "", "post_date": "2021-08-02T12:27:51.243000", "content": "

Thanks for the collection 😃 Unfortunately, you mislabeled the author for 1 and 5 🤕

", "votes": 1, "replies": [ { "id": 1409968, "author_name": "KritiDoneria", "author_url": "", "post_date": "2021-08-02T17:25:13.620000", "content": "

Thanks for pointing it out. Corrected 5, not so sure about 1.
\nThanks @furcifer

", "votes": 0, "replies": [] } ] }, { "id": 1406042, "author_name": "ElenaEB", "author_url": "", "post_date": "2021-07-31T12:34:32.130000", "content": "

whooohooo! and i agree with you, high praise to those who exemplify the spirit of the community!

", "votes": 1, "replies": [ { "id": 1409904, "author_name": "KritiDoneria", "author_url": "", "post_date": "2021-08-02T17:22:29.130000", "content": "

I agree @elenaeb

", "votes": 0, "replies": [] } ] }, { "id": 1396489, "author_name": "Keegan Fernandes", "author_url": "", "post_date": "2021-07-22T06:53:03.830000", "content": "

Thanks for mentioning my notebook😊. Good luck to all those taking part in the competition.👊

", "votes": 1, "replies": [ { "id": 1398016, "author_name": "KritiDoneria", "author_url": "", "post_date": "2021-07-23T17:24:26.047000", "content": "

all the best and thanks for sharing your work @aristotle609

", "votes": 1, "replies": [] } ] }, { "id": 1396620, "author_name": "tharun_01", "author_url": "", "post_date": "2021-07-22T09:43:05.107000", "content": "

Great collection to start, thank you.

", "votes": 2, "replies": [ { "id": 1398017, "author_name": "KritiDoneria", "author_url": "", "post_date": "2021-07-23T17:24:37.240000", "content": "

happy to help @tharun2001

", "votes": 1, "replies": [] } ] } ], "raw_markdown_by_id": { "1396020": "1. [An approach with PyTorch EfficientNet 3D](https://www.kaggle.com/xuxu1234/efficientnet3d-for-mri) by @xuxu1234 \n\n2. [A beginner friendly basic EDA](https://www.kaggle.com/aristotle609/basic-eda-rsna) by @aristotle609\n\n3. [A very detailed, publications heavy notebook](https://www.kaggle.com/mpwolke/glioblastoma-mri-dicom-format) by @mpwolke\n\n4. [Brain Tumor- EDA with Animations and Modeling](https://www.kaggle.com/ihelon/brain-tumor-eda-with-animations-and-modeling) by GM @ihelon . This one also has a great definition for competition metric\n\n5. [Basic Pytorch CNN Baseline](https://www.kaggle.com/furcifer/no-baseline-pytorch-cnn-for-mri) by @furcifer \n\n6. [A great notebook treating sequential image as video](https://www.kaggle.com/ayuraj/train-brain-tumor-as-video-classification-w-b) by @ayuraj\n\n7. [Using pre-trained models for baseline](https://www.kaggle.com/mrinath/using-pretrained-models-for-3d-data) by @mrinath . This is still work in progress.\n\n8. [Image augmentation](https://www.kaggle.com/furcifer/mri-data-augmentation-pipeline) by @furcifer\n\n9. [A no nonsense notebook for non-beginners](https://www.kaggle.com/hamzajebbar/brain-tumor-radiogenomic-classification) by @hamzajebbar\n\n10. [Effnet 3D from 1-D MRI](https://www.kaggle.com/rluethy/efficientnet3d-with-one-mri-type) by @rluethy\n\n\nA huge shoutout to all these people who risk their LB positions in the spirit of community.\nPlease upvote their notebooks if you find it useful.\nThanks", "1396822": "I want to add some of my \"favorites'\" :\n\nAll Images/Dicom collection from David Roberts in this competition: https://www.kaggle.com/davidbroberts till now 4 in RSNA/MICCAI and many in SIIM FISABIO. For those that wants to learn dcm files his work is a great source.\n\nBy YeongHyeon ( Convert DICOM to Numpy Array (Super Simple).\nBeautiful code, simple for him (not for me yet).\nhttps://www.kaggle.com/yeonghyeon/convert-dicom-to-numpy-array-super-simple/comments#1391881\n\nI made some amount of research, reading articles related to this Competition. And learnt many things about MGMT, Flair, T1 and T2 weighted.\nBut I credit all the Dicom script to Md Redwan Karim Sony and his contributions in RSNA STR Pulmonary Embolism Detection, like those below :\n\n https://www.kaggle.com/redwankarimsony/visualizing-and-analyzing-dicoms-in-python/notebook?select=train.csv \nhttps://www.kaggle.com/redwankarimsony/ct-scans-dicom-files-windowing-explained\n\nThank you Kriti, I'm flattered to be in your list.\n\n", "1408345": "Thanks for the collection 😃 Unfortunately, you mislabeled the author for 1 and 5 🤕", "1406042": "whooohooo! and i agree with you, high praise to those who exemplify the spirit of the community!", "1396489": "Thanks for mentioning my notebook😊. Good luck to all those taking part in the competition.👊", "1396620": "Great collection to start, thank you." } }