{ "id": 109261, "title": "Useful papers/links/data/top notebooks from 2018/2017 RSNA Competitions", "url": "/competitions/rsna-intracranial-hemorrhage-detection/discussion/109261", "author_name": "SeshuRaju 🧘‍♂️", "post_date": "2019-09-18T02:49:03.718000", "votes": 55, "comment_count": 11, "views": 0, "content": "

Hi All,\n*Home Page - https://www.rsna.org/

\n\n

Papers

\n\n

*Pediatric Bone Age Assessment Using Deep\nConvolutional Neural Networks - 2017 - https://arxiv.org/pdf/1712.05053.pdf\n*Decoding the Rejuvenating Effects of Mechanical Loading on Skeletal Maturation using in Vivo Imaging and Deep Learning - https://arxiv.org/pdf/1905.08099.pdf\n*The RSNA Pediatric Bone Age Machine Learning Challenge -https://pubs.rsna.org/doi/10.1148/radiol.2018180736\n*Performance of a Deep-Learning Neural Network Model in Assessing Skeletal Maturity on Pediatric Hand Radiographs - https://pubs.rsna.org/doi/pdf/10.1148/radiol.2018182657

\n\n

Data

\n\n\n\n

PreTrained Models

\n\n\n\n

2017/2018/2019 Leaderboards

\n\n\n\n

Top 5 solutions of 2018 competition

\n\n\n\n

keep post your useful links as comments. will update as competition goes. Welcome all and upvote if it useful to you. write a comment if you find useful material for this competition.

\n\n

The 2017 DSB used DICOM images: https://www.kaggle.com/c/data-science-bowl-2017/overview by @anjum48

\n\n

Good Luck to all

", "messages": [ { "id": 628849, "postDate": "2019-09-18T02:49:03.720Z", "content": "

Hi All,\n*Home Page - https://www.rsna.org/

\n\n

Papers

\n\n

*Pediatric Bone Age Assessment Using Deep\nConvolutional Neural Networks - 2017 - https://arxiv.org/pdf/1712.05053.pdf\n*Decoding the Rejuvenating Effects of Mechanical Loading on Skeletal Maturation using in Vivo Imaging and Deep Learning - https://arxiv.org/pdf/1905.08099.pdf\n*The RSNA Pediatric Bone Age Machine Learning Challenge -https://pubs.rsna.org/doi/10.1148/radiol.2018180736\n*Performance of a Deep-Learning Neural Network Model in Assessing Skeletal Maturity on Pediatric Hand Radiographs - https://pubs.rsna.org/doi/pdf/10.1148/radiol.2018182657

\n\n

Data

\n\n\n\n

PreTrained Models

\n\n\n\n

2017/2018/2019 Leaderboards

\n\n\n\n

Top 5 solutions of 2018 competition

\n\n\n\n

keep post your useful links as comments. will update as competition goes. Welcome all and upvote if it useful to you. write a comment if you find useful material for this competition.

\n\n

The 2017 DSB used DICOM images: https://www.kaggle.com/c/data-science-bowl-2017/overview by @anjum48

\n\n

Good Luck to all

", "rawMarkdown": "Hi All,\n*Home Page - https://www.rsna.org/\n\n## Papers\n*Pediatric Bone Age Assessment Using Deep\nConvolutional Neural Networks - 2017 - https://arxiv.org/pdf/1712.05053.pdf\n*Decoding the Rejuvenating Effects of Mechanical Loading on Skeletal Maturation using in Vivo Imaging and Deep Learning - https://arxiv.org/pdf/1905.08099.pdf\n*The RSNA Pediatric Bone Age Machine Learning Challenge -https://pubs.rsna.org/doi/10.1148/radiol.2018180736\n*Performance of a Deep-Learning Neural Network Model in Assessing Skeletal Maturity on Pediatric Hand Radiographs - https://pubs.rsna.org/doi/pdf/10.1148/radiol.2018182657\n\n## Data\n* MURA dataset: https://stanfordmlgroup.github.io/projects/mura/\n* Chest Xrays dataset on box (full set): https://nihcc.app.box.com/v/ChestXray-NIHCC\n* Chest Xrays dataset on kaggle (5% sample): https://www.kaggle.com/nih-chest-xrays/data\n* Bone Age dataset: https://stanfordmedicine.app.box.com/s/4r1zwio6z6lrzk7zw3fro7ql5mnoupcv/folder/42459416739\n* RSNA Bone Age on kaggle: https://www.kaggle.com/kmader/rsna-bone-age \n* Different architectures to try out: https://keras.io/applications/\n* 2017 RSNA pediatric bone age challenge: http://rsnachallenges.cloudapp.net/competitions/4\n* 16Bit challenge winner: https://www.16bit.ai/blog/ml-and-future-of-radiology\n\n## PreTrained Models\n* Pediatric Bone Age Prediction Github - https://github.com/lukaszbinden/pediatric-bone-age-prediction - credits to lukaszbinden\n* Keras Pretrained Models - https://www.kaggle.com/gaborfodor/keras-pretrained-models\n\n## 2017/2018/2019 Leaderboards\n* 2017 Leaderboard results - (http://rsnachallenges.cloudapp.net/competitions/4#learn_the_details-news)\n* 2018 leaderboard results (**4GB** data size) - https://www.kaggle.com/c/rsna-pneumonia-detection-challenge/leaderboard\n* 2019 leaderboard results(**130GB** data size) - https://www.kaggle.com/c/rsna-intracranial-hemorrhage-detection/leaderboard\n\n##Top 5 solutions of 2018 competition\n* 1st solution - https://www.kaggle.com/c/rsna-pneumonia-detection-challenge/discussion/70421#latest-496413\n code - https://www.github.com/i-pan/kaggle-rsna18\n* 2nd solution - https://www.kaggle.com/c/rsna-pneumonia-detection-challenge/discussion/70427#latest-497399\n code - https://github.com/yhenon/pytorch-retinanet\n* 3rd solution - https://www.kaggle.com/c/rsna-pneumonia-detection-challenge/discussion/70632#latest-440310\n code - https://github.com/pmcheng/rsna-pneumonia\n* 4th solution - https://www.kaggle.com/c/rsna-pneumonia-detection-challenge/discussion/71070#latest-525861\n code - https://github.com/DanielEftekhari/Machine-Learning-Presentations-Blogs/blob/master/Kaggle-RSNA-pneumonia-detection-challenge/16bit_layer6_RSNA_Pneumonia_Detection_Challenge_Winner_Documentation.pdf\n* 5th solution - https://www.kaggle.com/c/rsna-pneumonia-detection-challenge/discussion/79381#latest-465479\n code - https://github.com/JiYuanFeng/Kaggle_RSNA_Pneumonia-Detection-Challenge\n\n\nkeep post your useful links as comments. will update as competition goes. Welcome all and upvote if it useful to you. write a comment if you find useful material for this competition.\n\nThe 2017 DSB used DICOM images: https://www.kaggle.com/c/data-science-bowl-2017/overview by @anjum48\n\nGood Luck to all", "votes": 54 }, { "id": 629012, "postDate": "2019-09-18T08:23:44.757Z", "content": "

The 2017 DSB used DICOM images too: https://www.kaggle.com/c/data-science-bowl-2017/overview

\n\n

Might be some useful kernels in there!

", "rawMarkdown": "The 2017 DSB used DICOM images too: https://www.kaggle.com/c/data-science-bowl-2017/overview\n\nMight be some useful kernels in there!", "votes": 3 }, { "id": 629510, "postDate": "2019-09-18T21:19:18.330Z", "content": "

Nice, but this comp is completely different! ;)

", "rawMarkdown": "Nice, but this comp is completely different! ;)" }, { "id": 648443, "postDate": "2019-10-14T07:35:47.097Z", "content": "

good

", "rawMarkdown": "good" }, { "id": 628892, "postDate": "2019-09-18T04:52:14.310Z", "rawMarkdown": "", "isDeleted": true }, { "id": 633151, "postDate": "2019-09-24T13:43:15.547Z", "content": "

Nice! Thanks for sharing this!

", "rawMarkdown": "Nice! Thanks for sharing this!", "votes": 1 }, { "id": 651855, "postDate": "2019-10-18T02:50:27.170Z", "content": "

Thank you for sharing!

", "rawMarkdown": "Thank you for sharing!" }, { "id": 633437, "postDate": "2019-09-24T23:45:41.240Z", "content": "

Thank you for sharing!

", "rawMarkdown": "Thank you for sharing!" }, { "id": 630221, "postDate": "2019-09-19T23:46:54.823Z", "content": "

Thank you so much !

", "rawMarkdown": "Thank you so much !" }, { "id": 629456, "postDate": "2019-09-18T19:42:49.327Z", "content": "

Excellent!\nThanks for sharing.

", "rawMarkdown": "Excellent!\nThanks for sharing. " }, { "id": 629451, "postDate": "2019-09-18T19:38:12.820Z", "content": "

Thanks for sharing!!

", "rawMarkdown": "Thanks for sharing!!\n" }, { "id": 628932, "postDate": "2019-09-18T06:10:28.597Z", "content": "

Thanks for sharing.

", "rawMarkdown": "Thanks for sharing." } ], "comments": [ { "id": 629012, "author_name": "datasaurus", "author_url": "", "post_date": "2019-09-18T08:23:44.757000", "content": "

The 2017 DSB used DICOM images too: https://www.kaggle.com/c/data-science-bowl-2017/overview

\n\n

Might be some useful kernels in there!

", "votes": 3, "replies": [] }, { "id": 629510, "author_name": "Nanashi", "author_url": "", "post_date": "2019-09-18T21:19:18.330000", "content": "

Nice, but this comp is completely different! ;)

", "votes": 0, "replies": [] }, { "id": 648443, "author_name": "45848", "author_url": "", "post_date": "2019-10-14T07:35:47.097000", "content": "

good

", "votes": 0, "replies": [] }, { "id": 628892, "author_name": "", "author_url": "", "post_date": "2019-09-18T04:52:14.310000", "content": "", "votes": 0, "replies": [] }, { "id": 633151, "author_name": "Carlo Lepelaars", "author_url": "", "post_date": "2019-09-24T13:43:15.547000", "content": "

Nice! Thanks for sharing this!

", "votes": 1, "replies": [] }, { "id": 651855, "author_name": "LongYin/杰少", "author_url": "", "post_date": "2019-10-18T02:50:27.170000", "content": "

Thank you for sharing!

", "votes": 0, "replies": [] }, { "id": 633437, "author_name": "Rubens_Bolgheroni", "author_url": "", "post_date": "2019-09-24T23:45:41.240000", "content": "

Thank you for sharing!

", "votes": 0, "replies": [] }, { "id": 630221, "author_name": "Chahine Haji", "author_url": "", "post_date": "2019-09-19T23:46:54.823000", "content": "

Thank you so much !

", "votes": 0, "replies": [] }, { "id": 629456, "author_name": "Eric M. Baumel", "author_url": "", "post_date": "2019-09-18T19:42:49.327000", "content": "

Excellent!\nThanks for sharing.

", "votes": 0, "replies": [] }, { "id": 629451, "author_name": "Niranjan Agnihotri", "author_url": "", "post_date": "2019-09-18T19:38:12.820000", "content": "

Thanks for sharing!!

", "votes": 0, "replies": [] }, { "id": 628932, "author_name": "NeerajSharma", "author_url": "", "post_date": "2019-09-18T06:10:28.597000", "content": "

Thanks for sharing.

", "votes": 0, "replies": [] } ], "raw_markdown_by_id": { "628849": "Hi All,\n*Home Page - https://www.rsna.org/\n\n## Papers\n*Pediatric Bone Age Assessment Using Deep\nConvolutional Neural Networks - 2017 - https://arxiv.org/pdf/1712.05053.pdf\n*Decoding the Rejuvenating Effects of Mechanical Loading on Skeletal Maturation using in Vivo Imaging and Deep Learning - https://arxiv.org/pdf/1905.08099.pdf\n*The RSNA Pediatric Bone Age Machine Learning Challenge -https://pubs.rsna.org/doi/10.1148/radiol.2018180736\n*Performance of a Deep-Learning Neural Network Model in Assessing Skeletal Maturity on Pediatric Hand Radiographs - https://pubs.rsna.org/doi/pdf/10.1148/radiol.2018182657\n\n## Data\n* MURA dataset: https://stanfordmlgroup.github.io/projects/mura/\n* Chest Xrays dataset on box (full set): https://nihcc.app.box.com/v/ChestXray-NIHCC\n* Chest Xrays dataset on kaggle (5% sample): https://www.kaggle.com/nih-chest-xrays/data\n* Bone Age dataset: https://stanfordmedicine.app.box.com/s/4r1zwio6z6lrzk7zw3fro7ql5mnoupcv/folder/42459416739\n* RSNA Bone Age on kaggle: https://www.kaggle.com/kmader/rsna-bone-age \n* Different architectures to try out: https://keras.io/applications/\n* 2017 RSNA pediatric bone age challenge: http://rsnachallenges.cloudapp.net/competitions/4\n* 16Bit challenge winner: https://www.16bit.ai/blog/ml-and-future-of-radiology\n\n## PreTrained Models\n* Pediatric Bone Age Prediction Github - https://github.com/lukaszbinden/pediatric-bone-age-prediction - credits to lukaszbinden\n* Keras Pretrained Models - https://www.kaggle.com/gaborfodor/keras-pretrained-models\n\n## 2017/2018/2019 Leaderboards\n* 2017 Leaderboard results - (http://rsnachallenges.cloudapp.net/competitions/4#learn_the_details-news)\n* 2018 leaderboard results (**4GB** data size) - https://www.kaggle.com/c/rsna-pneumonia-detection-challenge/leaderboard\n* 2019 leaderboard results(**130GB** data size) - https://www.kaggle.com/c/rsna-intracranial-hemorrhage-detection/leaderboard\n\n##Top 5 solutions of 2018 competition\n* 1st solution - https://www.kaggle.com/c/rsna-pneumonia-detection-challenge/discussion/70421#latest-496413\n code - https://www.github.com/i-pan/kaggle-rsna18\n* 2nd solution - https://www.kaggle.com/c/rsna-pneumonia-detection-challenge/discussion/70427#latest-497399\n code - https://github.com/yhenon/pytorch-retinanet\n* 3rd solution - https://www.kaggle.com/c/rsna-pneumonia-detection-challenge/discussion/70632#latest-440310\n code - https://github.com/pmcheng/rsna-pneumonia\n* 4th solution - https://www.kaggle.com/c/rsna-pneumonia-detection-challenge/discussion/71070#latest-525861\n code - https://github.com/DanielEftekhari/Machine-Learning-Presentations-Blogs/blob/master/Kaggle-RSNA-pneumonia-detection-challenge/16bit_layer6_RSNA_Pneumonia_Detection_Challenge_Winner_Documentation.pdf\n* 5th solution - https://www.kaggle.com/c/rsna-pneumonia-detection-challenge/discussion/79381#latest-465479\n code - https://github.com/JiYuanFeng/Kaggle_RSNA_Pneumonia-Detection-Challenge\n\n\nkeep post your useful links as comments. will update as competition goes. Welcome all and upvote if it useful to you. write a comment if you find useful material for this competition.\n\nThe 2017 DSB used DICOM images: https://www.kaggle.com/c/data-science-bowl-2017/overview by @anjum48\n\nGood Luck to all", "629012": "The 2017 DSB used DICOM images too: https://www.kaggle.com/c/data-science-bowl-2017/overview\n\nMight be some useful kernels in there!", "629510": "Nice, but this comp is completely different! ;)", "648443": "good", "628892": "", "633151": "Nice! Thanks for sharing this!", "651855": "Thank you for sharing!", "633437": "Thank you for sharing!", "630221": "Thank you so much !", "629456": "Excellent!\nThanks for sharing. ", "629451": "Thanks for sharing!!\n", "628932": "Thanks for sharing." } }