I have a lot of questions in my mind, Hope someone could help. Here Each person is provided with an FVC through some weeks and a CT scan for each week. What is the advantage of having a CT scan if we are given FVC do this provide any new features to the data. Ok lets consider we get some meta data from the images , later we hv combined the given patient info to the meta data acquired from the images. Now how do we train the model what would be the targets here as we have several FVC for each patient which FVC would become the target for my training set. should we do some thing like a time series analysis where we use the lag to predict the future? These are the doubts in my mind . Could someone help? thank you in advance .
", "messages": [ { "id": "990386", "postDate": "08/29/2020 15:16:13", "content": "I have a lot of questions in my mind, Hope someone could help. Here Each person is provided with an FVC through some weeks and a CT scan for each week. What is the advantage of having a CT scan if we are given FVC do this provide any new features to the data. Ok lets consider we get some meta data from the images , later we hv combined the given patient info to the meta data acquired from the images. Now how do we train the model what would be the targets here as we have several FVC for each patient which FVC would become the target for my training set. should we do some thing like a time series analysis where we use the lag to predict the future? These are the doubts in my mind . Could someone help? thank you in advance .
", "rawMarkdown": "I have a lot of questions in my mind, Hope someone could help. Here Each person is provided with an FVC through some weeks and a CT scan for each week. What is the advantage of having a CT scan if we are given FVC do this provide any new features to the data. Ok lets consider we get some meta data from the images , later we hv combined the given patient info to the meta data acquired from the images. Now how do we train the model what would be the targets here as we have several FVC for each patient which FVC would become the target for my training set. should we do some thing like a time series analysis where we use the lag to predict the future? These are the doubts in my mind . Could someone help? thank you in advance .", "votes": null }, { "id": "990389", "postDate": "08/29/2020 15:19:06", "content": "I was wondering the same. I believe we need Time Series analysis combined with a ConvNet, however I can't imagine how would that work out. I think this is the main reason that much of the Public notebooks only use Meta features in a Tabular net.
", "rawMarkdown": "I was wondering the same. I believe we need Time Series analysis combined with a ConvNet, however I can't imagine how would that work out. I think this is the main reason that much of the Public notebooks only use Meta features in a Tabular net.", "votes": null }, { "id": "990399", "postDate": "08/29/2020 15:24:33", "content": "Did u try any approach on the data? I have read some kernels but after some point where data gathering starts from images I get lost . I couldnt understand whats happening lol..
", "rawMarkdown": "Did u try any approach on the data? I have read some kernels but after some point where data gathering starts from images I get lost . I couldnt understand whats happening lol..", "votes": null }, { "id": "990408", "postDate": "08/29/2020 15:27:29", "content": "I am still in the process of experimenting, but I am only working on meta-features though. I too can't understand how you would go about using the images.
", "rawMarkdown": "I am still in the process of experimenting, but I am only working on meta-features though. I too can't understand how you would go about using the images.", "votes": null }, { "id": "990513", "postDate": "08/29/2020 16:56:01", "content": "Hi! @raviet, here is how I see the problem, that defers from your explanation.
\nFirst, we have only 1 CT on baseline week.
\n\"In the dataset, you are provided with a baseline chest CT scan and associated clinical information for a set of patients. A patient has an image acquired at time Week = 0 and has numerous follow-up visits over the course of approximately 1-2 years, at which time their FVC is measured.\"
The goal of this competition, I believe, is to solve a problem in regards to predicting the condition of the patient in the future based on 1 CT and multiple follow-ups using the FVC test.
\nCT scanners are expensive and time-consuming, so I think the team at OSIC is looking for a proxy that can help without the necessity of completing too many CT scans, maybe only on patients where the model predicts high confidence of the disease.
\nThis is the way I'm approaching this challenge.
\nI hope this help. If you have more questions let me know.
", "rawMarkdown": "Hi! @raviet, here is how I see the problem, that defers from your explanation.\nFirst, we have only 1 CT on baseline week.\n\"In the dataset, you are provided with a baseline chest CT scan and associated clinical information for a set of patients. A patient has an image acquired at time Week = 0 and has numerous follow-up visits over the course of approximately 1-2 years, at which time their FVC is measured.\"\n\nThe goal of this competition, I believe, is to solve a problem in regards to predicting the condition of the patient in the future based on 1 CT and multiple follow-ups using the FVC test.\n\nCT scanners are expensive and time-consuming, so I think the team at OSIC is looking for a proxy that can help without the necessity of completing too many CT scans, maybe only on patients where the model predicts high confidence of the disease.\n\nThis is the way I'm approaching this challenge.\n1. Extract useful information from the patient CT (CNN or Another Method, Metadata).\n2. Build a Regression Model with the Patient's FVC data.\n3. Merge Both Models and Predict for a certain number of weeks into the future.\n\nI hope this help. If you have more questions let me know.", "votes": null } ], "comments": [ { "id": 990389, "author_name": "heyytanay", "author_url": "", "post_date": "08/29/2020 15:19:06", "content": "I was wondering the same. I believe we need Time Series analysis combined with a ConvNet, however I can't imagine how would that work out. I think this is the main reason that much of the Public notebooks only use Meta features in a Tabular net.
", "votes": null, "replies": [ { "id": 990399, "author_name": "raviet", "author_url": "", "post_date": "08/29/2020 15:24:33", "content": "Did u try any approach on the data? I have read some kernels but after some point where data gathering starts from images I get lost . I couldnt understand whats happening lol..
", "votes": null, "replies": [] }, { "id": 990408, "author_name": "heyytanay", "author_url": "", "post_date": "08/29/2020 15:27:29", "content": "I am still in the process of experimenting, but I am only working on meta-features though. I too can't understand how you would go about using the images.
", "votes": null, "replies": [] } ] }, { "id": 990513, "author_name": "cv13j0", "author_url": "", "post_date": "08/29/2020 16:56:01", "content": "Hi! @raviet, here is how I see the problem, that defers from your explanation.
\nFirst, we have only 1 CT on baseline week.
\n\"In the dataset, you are provided with a baseline chest CT scan and associated clinical information for a set of patients. A patient has an image acquired at time Week = 0 and has numerous follow-up visits over the course of approximately 1-2 years, at which time their FVC is measured.\"
The goal of this competition, I believe, is to solve a problem in regards to predicting the condition of the patient in the future based on 1 CT and multiple follow-ups using the FVC test.
\nCT scanners are expensive and time-consuming, so I think the team at OSIC is looking for a proxy that can help without the necessity of completing too many CT scans, maybe only on patients where the model predicts high confidence of the disease.
\nThis is the way I'm approaching this challenge.
\nI hope this help. If you have more questions let me know.
", "votes": null, "replies": [] } ], "raw_markdown_by_id": { "990386": "I have a lot of questions in my mind, Hope someone could help. Here Each person is provided with an FVC through some weeks and a CT scan for each week. What is the advantage of having a CT scan if we are given FVC do this provide any new features to the data. Ok lets consider we get some meta data from the images , later we hv combined the given patient info to the meta data acquired from the images. Now how do we train the model what would be the targets here as we have several FVC for each patient which FVC would become the target for my training set. should we do some thing like a time series analysis where we use the lag to predict the future? These are the doubts in my mind . Could someone help? thank you in advance .", "990389": "I was wondering the same. I believe we need Time Series analysis combined with a ConvNet, however I can't imagine how would that work out. I think this is the main reason that much of the Public notebooks only use Meta features in a Tabular net.", "990399": "Did u try any approach on the data? I have read some kernels but after some point where data gathering starts from images I get lost . I couldnt understand whats happening lol..", "990408": "I am still in the process of experimenting, but I am only working on meta-features though. I too can't understand how you would go about using the images.", "990513": "Hi! @raviet, here is how I see the problem, that defers from your explanation.\nFirst, we have only 1 CT on baseline week.\n\"In the dataset, you are provided with a baseline chest CT scan and associated clinical information for a set of patients. A patient has an image acquired at time Week = 0 and has numerous follow-up visits over the course of approximately 1-2 years, at which time their FVC is measured.\"\n\nThe goal of this competition, I believe, is to solve a problem in regards to predicting the condition of the patient in the future based on 1 CT and multiple follow-ups using the FVC test.\n\nCT scanners are expensive and time-consuming, so I think the team at OSIC is looking for a proxy that can help without the necessity of completing too many CT scans, maybe only on patients where the model predicts high confidence of the disease.\n\nThis is the way I'm approaching this challenge.\n1. Extract useful information from the patient CT (CNN or Another Method, Metadata).\n2. Build a Regression Model with the Patient's FVC data.\n3. Merge Both Models and Predict for a certain number of weeks into the future.\n\nI hope this help. If you have more questions let me know." }, "source": "meta" }