\n
![\"\"](\"https://i.imgur.com/GhT4iks.png\")
I experimented a bit with the data for this competition. I tried to use the data \"as is\" - and push it just into the EffNetB0.
\nI took 8 slices (with equal steps) from each of the first three views of the structural multi-parametric MRI (mpMRI) scans:
\nThen I take a mean of slices of each type of scan and get 3 \"channels\":
\n
Then merge them into one \"image\" and push them to the EfficientNetB0 model.
\nI trained it 2 epochs and after the second I have the next metrics:
\n[Epoch Train: 2] loss: 0.68856, score: 0.56197, time: 50 s\n[Epoch Valid: 2] loss: 0.70479, score: 0.52137, time: 12 s\nAs we can see my loss is equal to ~0.69. It means that the model is not good and the LB score is overfitted.
\nFrom this experiment, I think this approach doesn't have the chance of success. Further improvement of the algorithm should be carried out in two directions:
\nEDA and starter modeling you can find in my notebook:
\n", "messages": [ { "id": 1388374, "postDate": "2021-07-14T21:57:44.330Z", "content": "I experimented a bit with the data for this competition. I tried to use the data \"as is\" - and push it just into the EffNetB0.
\nI took 8 slices (with equal steps) from each of the first three views of the structural multi-parametric MRI (mpMRI) scans:
\nThen I take a mean of slices of each type of scan and get 3 \"channels\":
\n
Then merge them into one \"image\" and push them to the EfficientNetB0 model.
\nI trained it 2 epochs and after the second I have the next metrics:
\n[Epoch Train: 2] loss: 0.68856, score: 0.56197, time: 50 s\n[Epoch Valid: 2] loss: 0.70479, score: 0.52137, time: 12 s\nAs we can see my loss is equal to ~0.69. It means that the model is not good and the LB score is overfitted.
\nFrom this experiment, I think this approach doesn't have the chance of success. Further improvement of the algorithm should be carried out in two directions:
\nEDA and starter modeling you can find in my notebook:
\n", "rawMarkdown": "I experimented a bit with the data for this competition. I tried to use the data \"as is\" - and push it just into the EffNetB0.\n\nI took 8 slices (with equal steps) from each of the first three views of the structural multi-parametric MRI (mpMRI) scans:\n- Fluid Attenuated Inversion Recovery (FLAIR)\n- T1-weighted pre-contrast (T1w)\n- T1-weighted post-contrast (T1Gd)\n\nThen I take a mean of slices of each type of scan and get 3 \"channels\": \n\n\nThen merge them into one \"image\" and push them to the EfficientNetB0 model.\n\nI trained it 2 epochs and after the second I have the next metrics:\n```\n[Epoch Train: 2] loss: 0.68856, score: 0.56197, time: 50 s\n[Epoch Valid: 2] loss: 0.70479, score: 0.52137, time: 12 s\n```\nAs we can see my loss is equal to ~0.69. It means that the model is not good and the LB score is overfitted.\n\nFrom this experiment, I think this approach doesn't have the chance of success. Further improvement of the algorithm should be carried out in two directions:\n- To use models that can accept 3D scans as input and are specially pre-trained for medical tasks.\n- Cleaning and working with data, as the scans have different tilt angles and parameters.\n\nEDA and starter modeling you can find in my notebook:\n- [EDA & train & inference](https://www.kaggle.com/ihelon/brain-tumor-eda-with-animations-and-modeling)", "votes": 27 }, { "id": 1390455, "postDate": "2021-07-16T16:56:04.330Z", "content": "Hi,
\nI think preprocessing the data in order to make them comparable (e.g. different plane) is going to be a key challenge here.
", "rawMarkdown": "Hi,\n\nI think preprocessing the data in order to make them comparable (e.g. different plane) is going to be a key challenge here. ", "votes": 3, "replies": [ { "id": 1390479, "postDate": "2021-07-16T17:12:51.160Z", "content": "Yes, it is a good point.
", "rawMarkdown": "Yes, it is a good point." }, { "id": 1390748, "postDate": "2021-07-17T04:08:39.227Z", "content": "Luca, what do you mean? Can you explain more thoroughly and give some reference?
", "rawMarkdown": "Luca, what do you mean? Can you explain more thoroughly and give some reference?" }, { "id": 1391195, "postDate": "2021-07-17T11:35:15.517Z", "content": "You have a sequence of picture representing a 3D-Model of the brain.
\nIs is shown clearly in Yaroslavs notebook. In just a few of the pictures the tumor is seen.
\nThen not every patient has the same plane:
\nhttps://www.kaggle.com/davidbroberts/determining-mr-image-planes
\nSo in may point of view the first goal is going to be to preprocess the data in a way to make them comparable in order that a neural network can extract relevant information.
\nA 3D-Reconstraction may an approach.
\nUnfortunately I dont know yet how to do this.
", "rawMarkdown": "You have a sequence of picture representing a 3D-Model of the brain. \n\nIs is shown clearly in Yaroslavs notebook. In just a few of the pictures the tumor is seen. \n\nThen not every patient has the same plane:\n\nhttps://www.kaggle.com/davidbroberts/determining-mr-image-planes\n\nSo in may point of view the first goal is going to be to preprocess the data in a way to make them comparable in order that a neural network can extract relevant information.\n\nA 3D-Reconstraction may an approach. \n\nUnfortunately I dont know yet how to do this.", "votes": 3 } ] }, { "id": 1390391, "postDate": "2021-07-16T15:59:26.537Z", "content": "Congrats @ihelon on becoming Grandmaster!!!!, you deserve it, your notebooks deserve it
", "rawMarkdown": "Congrats @ihelon on becoming Grandmaster!!!!, you deserve it, your notebooks deserve it", "votes": 1, "replies": [ { "id": 1390424, "postDate": "2021-07-16T16:21:44.097Z", "content": "@mrinath Thank you! I will try to do it better and better!๐
", "rawMarkdown": "@mrinath Thank you! I will try to do it better and better!๐", "votes": 1 } ] }, { "id": 1388837, "postDate": "2021-07-15T08:55:28.160Z", "content": "In the given 3 images, which one is the merged image? or you use these 3 to make a merged image?
", "rawMarkdown": "In the given 3 images, which one is the merged image? or you use these 3 to make a merged image?", "votes": 1, "replies": [ { "id": 1388844, "postDate": "2021-07-15T09:02:42.217Z", "content": "@mrinath each of the images on the graph is merged by selected scan type:
\nThey look similar, but it is problem with the scan and I think we need to preprocess scans before any train procedures.
", "rawMarkdown": "@mrinath each of the images on the graph is merged by selected scan type: \n- Fluid Attenuated Inversion Recovery (FLAIR)\n- T1-weighted pre-contrast (T1w)\n- T1-weighted post-contrast (T1Gd)\n\nThey look similar, but it is problem with the scan and I think we need to preprocess scans before any train procedures.", "votes": 2 }, { "id": 1389852, "postDate": "2021-07-16T06:17:05.110Z", "content": "why you are using only three categories? @ihelon any reason behind that?
", "rawMarkdown": "why you are using only three categories? @ihelon any reason behind that?", "votes": 1 }, { "id": 1389983, "postDate": "2021-07-16T08:56:26.883Z", "content": "@santhoshkumarv because the default EffNet model works with a three-channel image.
", "rawMarkdown": "@santhoshkumarv because the default EffNet model works with a three-channel image." } ] }, { "id": 1388547, "postDate": "2021-07-15T04:12:29.077Z", "content": "Thanks for such a great notebook.
\nIf understood correctly you took (FLAIR, T1w, T1Gd) these three folders of images and experimented.
\nDid you resize the images and take out the blank images?
Thanks!
\nYes, it's rigth.
\nI resized them to 256*256 but I don't filter blank images. It's should be a little bit harder implementation. And I don't touch images on the corners (I think many blank images are there).
Hi,
\nI think preprocessing the data in order to make them comparable (e.g. different plane) is going to be a key challenge here.
", "votes": 3, "replies": [ { "id": 1390479, "author_name": "Yaroslav Isaienkov", "author_url": "", "post_date": "2021-07-16T17:12:51.160000", "content": "Yes, it is a good point.
", "votes": 0, "replies": [] }, { "id": 1390748, "author_name": "Arslanov", "author_url": "", "post_date": "2021-07-17T04:08:39.227000", "content": "Luca, what do you mean? Can you explain more thoroughly and give some reference?
", "votes": 0, "replies": [] }, { "id": 1391195, "author_name": "LucaMTB", "author_url": "", "post_date": "2021-07-17T11:35:15.517000", "content": "You have a sequence of picture representing a 3D-Model of the brain.
\nIs is shown clearly in Yaroslavs notebook. In just a few of the pictures the tumor is seen.
\nThen not every patient has the same plane:
\nhttps://www.kaggle.com/davidbroberts/determining-mr-image-planes
\nSo in may point of view the first goal is going to be to preprocess the data in a way to make them comparable in order that a neural network can extract relevant information.
\nA 3D-Reconstraction may an approach.
\nUnfortunately I dont know yet how to do this.
", "votes": 3, "replies": [] } ] }, { "id": 1390391, "author_name": "DeepUnderstanding", "author_url": "", "post_date": "2021-07-16T15:59:26.537000", "content": "Congrats @ihelon on becoming Grandmaster!!!!, you deserve it, your notebooks deserve it
", "votes": 1, "replies": [ { "id": 1390424, "author_name": "Yaroslav Isaienkov", "author_url": "", "post_date": "2021-07-16T16:21:44.097000", "content": "@mrinath Thank you! I will try to do it better and better!๐
", "votes": 1, "replies": [] } ] }, { "id": 1388837, "author_name": "DeepUnderstanding", "author_url": "", "post_date": "2021-07-15T08:55:28.160000", "content": "In the given 3 images, which one is the merged image? or you use these 3 to make a merged image?
", "votes": 1, "replies": [ { "id": 1388844, "author_name": "Yaroslav Isaienkov", "author_url": "", "post_date": "2021-07-15T09:02:42.217000", "content": "@mrinath each of the images on the graph is merged by selected scan type:
\nThey look similar, but it is problem with the scan and I think we need to preprocess scans before any train procedures.
", "votes": 2, "replies": [] }, { "id": 1389852, "author_name": "Santhoshkumar", "author_url": "", "post_date": "2021-07-16T06:17:05.110000", "content": "why you are using only three categories? @ihelon any reason behind that?
", "votes": 1, "replies": [] }, { "id": 1389983, "author_name": "Yaroslav Isaienkov", "author_url": "", "post_date": "2021-07-16T08:56:26.883000", "content": "@santhoshkumarv because the default EffNet model works with a three-channel image.
", "votes": 0, "replies": [] } ] }, { "id": 1388547, "author_name": "tharun_01", "author_url": "", "post_date": "2021-07-15T04:12:29.077000", "content": "Thanks for such a great notebook.
\nIf understood correctly you took (FLAIR, T1w, T1Gd) these three folders of images and experimented.
\nDid you resize the images and take out the blank images?
Thanks!
\nYes, it's rigth.
\nI resized them to 256*256 but I don't filter blank images. It's should be a little bit harder implementation. And I don't touch images on the corners (I think many blank images are there).