Dear Kagglers,
\nThank you for you participation and input into such a wonderful competition. It was very exciting to see so many of you, perhaps unfamiliar with the clinical scenario of placing lines and tubes in very sick hospital patients, passionately trying to develop the best model for predicting optimal positioning of these lines and tubes.
\nAs we look through solutions, we anticipate that strong performers in this competition may be invited to contribute to the RANZCR Annual Scientific (ASM) meeting associated with this competition with limited lodging and conference support to present their solutions. As this will be a hybrid in person and virtual meeting, the chosen top Kagglers can also choose to present their solutions either in person or virtually. The RANZCR ASM will take place from Sept 16-19 at the Melbourne Convention Centre. At this conference you will learn more about the development of medical imaging AI models for real life applications, meet colleagues & industry leaders with clinical AI solutions, data scientists and experts in AI who will discuss hot topics related to “safe AI, hidden stratification, inherent bias etc”. You can find out more information about the conference here: https://www.ranzcr.com/whats-on/events/ranzcr-annual-scientific-meeting-2021.
\nWe also encourage you to share your notebooks with a short write-up of how to run the code, such that other data scientists and clinicians can take advantage of your model to design a better model to predict optimal placement of lines and tubes.
\nIf you have questions, of course, feel free post in the discussion. We hope that your efforts will help save many lives of sick patients as we gradually come out of this global pandemic. Thank you for your efforts and for the positive comments on this challenge.
\nCheers,
\nMeng, Jennifer and Jarrel
", "messages": [ { "id": "1249018", "postDate": "03/23/2021 02:59:06", "content": "Dear Kagglers,
\nThank you for you participation and input into such a wonderful competition. It was very exciting to see so many of you, perhaps unfamiliar with the clinical scenario of placing lines and tubes in very sick hospital patients, passionately trying to develop the best model for predicting optimal positioning of these lines and tubes.
\nAs we look through solutions, we anticipate that strong performers in this competition may be invited to contribute to the RANZCR Annual Scientific (ASM) meeting associated with this competition with limited lodging and conference support to present their solutions. As this will be a hybrid in person and virtual meeting, the chosen top Kagglers can also choose to present their solutions either in person or virtually. The RANZCR ASM will take place from Sept 16-19 at the Melbourne Convention Centre. At this conference you will learn more about the development of medical imaging AI models for real life applications, meet colleagues & industry leaders with clinical AI solutions, data scientists and experts in AI who will discuss hot topics related to “safe AI, hidden stratification, inherent bias etc”. You can find out more information about the conference here: https://www.ranzcr.com/whats-on/events/ranzcr-annual-scientific-meeting-2021.
\nWe also encourage you to share your notebooks with a short write-up of how to run the code, such that other data scientists and clinicians can take advantage of your model to design a better model to predict optimal placement of lines and tubes.
\nIf you have questions, of course, feel free post in the discussion. We hope that your efforts will help save many lives of sick patients as we gradually come out of this global pandemic. Thank you for your efforts and for the positive comments on this challenge.
\nCheers,
\nMeng, Jennifer and Jarrel
", "rawMarkdown": "Dear Kagglers,\n\nThank you for you participation and input into such a wonderful competition. It was very exciting to see so many of you, perhaps unfamiliar with the clinical scenario of placing lines and tubes in very sick hospital patients, passionately trying to develop the best model for predicting optimal positioning of these lines and tubes.\n\nAs we look through solutions, we anticipate that strong performers in this competition may be invited to contribute to the RANZCR Annual Scientific (ASM) meeting associated with this competition with limited lodging and conference support to present their solutions. As this will be a hybrid in person and virtual meeting, the chosen top Kagglers can also choose to present their solutions either in person or virtually. The RANZCR ASM will take place from Sept 16-19 at the Melbourne Convention Centre. At this conference you will learn more about the development of medical imaging AI models for real life applications, meet colleagues & industry leaders with clinical AI solutions, data scientists and experts in AI who will discuss hot topics related to “safe AI, hidden stratification, inherent bias etc”. You can find out more information about the conference here: https://www.ranzcr.com/whats-on/events/ranzcr-annual-scientific-meeting-2021.\n\nWe also encourage you to share your notebooks with a short write-up of how to run the code, such that other data scientists and clinicians can take advantage of your model to design a better model to predict optimal placement of lines and tubes. \n\nIf you have questions, of course, feel free post in the discussion. We hope that your efforts will help save many lives of sick patients as we gradually come out of this global pandemic. Thank you for your efforts and for the positive comments on this challenge.\n\nCheers,\n\nMeng, Jennifer and Jarrel", "votes": null }, { "id": "1279713", "postDate": "04/21/2021 07:16:00", "content": "Hello @menglaw,
\nI have participated in this competition, and I was wondering if you could provide the test data used to evaluate the models. I would like to try to push this topic further in my research and possibly propose and simpler method. Thank you.
Thank you Melissa and all our Kagglers for your interest and participation our competition - to preserve the integrity of the test set for future work, we are not planning on releasing the test set publicly at this time. However, we would be happy to work with researchers privately to help evaluate their models if you would like to send us your model as a Docker container. Interested researchers can contact us at ranzcrai@gmail.com
", "rawMarkdown": "Thank you Melissa and all our Kagglers for your interest and participation our competition - to preserve the integrity of the test set for future work, we are not planning on releasing the test set publicly at this time. However, we would be happy to work with researchers privately to help evaluate their models if you would like to send us your model as a Docker container. Interested researchers can contact us at ranzcrai@gmail.com", "votes": null } ], "comments": [ { "id": 1279713, "author_name": "melissabeaini", "author_url": "", "post_date": "04/21/2021 07:16:00", "content": "Hello @menglaw,
\nI have participated in this competition, and I was wondering if you could provide the test data used to evaluate the models. I would like to try to push this topic further in my research and possibly propose and simpler method. Thank you.
Thank you Melissa and all our Kagglers for your interest and participation our competition - to preserve the integrity of the test set for future work, we are not planning on releasing the test set publicly at this time. However, we would be happy to work with researchers privately to help evaluate their models if you would like to send us your model as a Docker container. Interested researchers can contact us at ranzcrai@gmail.com
", "votes": null, "replies": [] } ] } ], "raw_markdown_by_id": { "1249018": "Dear Kagglers,\n\nThank you for you participation and input into such a wonderful competition. It was very exciting to see so many of you, perhaps unfamiliar with the clinical scenario of placing lines and tubes in very sick hospital patients, passionately trying to develop the best model for predicting optimal positioning of these lines and tubes.\n\nAs we look through solutions, we anticipate that strong performers in this competition may be invited to contribute to the RANZCR Annual Scientific (ASM) meeting associated with this competition with limited lodging and conference support to present their solutions. As this will be a hybrid in person and virtual meeting, the chosen top Kagglers can also choose to present their solutions either in person or virtually. The RANZCR ASM will take place from Sept 16-19 at the Melbourne Convention Centre. At this conference you will learn more about the development of medical imaging AI models for real life applications, meet colleagues & industry leaders with clinical AI solutions, data scientists and experts in AI who will discuss hot topics related to “safe AI, hidden stratification, inherent bias etc”. You can find out more information about the conference here: https://www.ranzcr.com/whats-on/events/ranzcr-annual-scientific-meeting-2021.\n\nWe also encourage you to share your notebooks with a short write-up of how to run the code, such that other data scientists and clinicians can take advantage of your model to design a better model to predict optimal placement of lines and tubes. \n\nIf you have questions, of course, feel free post in the discussion. We hope that your efforts will help save many lives of sick patients as we gradually come out of this global pandemic. Thank you for your efforts and for the positive comments on this challenge.\n\nCheers,\n\nMeng, Jennifer and Jarrel", "1279713": "Hello @menglaw,\nI have participated in this competition, and I was wondering if you could provide the test data used to evaluate the models. I would like to try to push this topic further in my research and possibly propose and simpler method. Thank you.", "1280403": "Thank you Melissa and all our Kagglers for your interest and participation our competition - to preserve the integrity of the test set for future work, we are not planning on releasing the test set publicly at this time. However, we would be happy to work with researchers privately to help evaluate their models if you would like to send us your model as a Docker container. Interested researchers can contact us at ranzcrai@gmail.com" }, "source": "meta" }