How to increase validation accuracy on medical images
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I'm working on a medical Xray images dataset trying to do a binary classification.
After many tries, I have a found a model that can overfit my training set with > 99% accuracy but from the validation curve look it seems like my model has only learned irrelevant details.
What do you think ?
When I try to introduce dropout, training become incredibly slow with bad acc.
If I try image augmentation, results are more promising but of course much slower.
I wonder what to look next:
- try running more epochs on the image augmented model
- try some medical pretrained model (do you know where to look)
What would you use as parameters for image augmentation (preferably in Keras) with Xray images ?
tensorflow keras computer-vision medical
asked Nov 11 at 8:50
S Dufour
152
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up vote
1
down vote
favorite
I'm working on a medical Xray images dataset trying to do a binary classification.
After many tries, I have a found a model that can overfit my training set with > 99% accuracy but from the validation curve look it seems like my model has only learned irrelevant details.
What do you think ?
When I try to introduce dropout, training become incredibly slow with bad acc.
If I try image augmentation, results are more promising but of course much slower.
I wonder what to look next:
- try running more epochs on the image augmented model
- try some medical pretrained model (do you know where to look)
What would you use as parameters for image augmentation (preferably in Keras) with Xray images ?
tensorflow keras computer-vision medical
asked Nov 11 at 8:50
S Dufour
152
To establish, how many images are you using for training and test data? There are many reasons why the validation accuracy may not be rising, but would be good to know how many images you are training on, as well as what types of x-ray images you are working with.
– Michael Grogan
Nov 11 at 15:09
Hi, I have 40k images in training and 3.6k in validation with 60.8% of negative examples. Those are Musculoskeletal Radiographs of Elbow, Finger, Forearm and Humerus. They are available in RGB but all the channels are equal.
– S Dufour
Nov 11 at 15:37
While health itself is not my expertise, have you accounted for voxel spacing? Essentially, since you are working with health data, it is likely that you would need to implement spatial normalisation, since this would allow the algorithm to better classify each image while accounting for voxel spacing. This article might provide you with more insights: medium.com/tensorflow/…
– Michael Grogan
Nov 14 at 15:18
add a comment |
up vote
1
down vote
favorite
up vote
1
down vote
favorite
I'm working on a medical Xray images dataset trying to do a binary classification.
After many tries, I have a found a model that can overfit my training set with > 99% accuracy but from the validation curve look it seems like my model has only learned irrelevant details.
What do you think ?
When I try to introduce dropout, training become incredibly slow with bad acc.
If I try image augmentation, results are more promising but of course much slower.
I wonder what to look next:
- try running more epochs on the image augmented model
- try some medical pretrained model (do you know where to look)
What would you use as parameters for image augmentation (preferably in Keras) with Xray images ?
tensorflow keras computer-vision medical
asked Nov 11 at 8:50
S Dufour
152
I'm working on a medical Xray images dataset trying to do a binary classification.
After many tries, I have a found a model that can overfit my training set with > 99% accuracy but from the validation curve look it seems like my model has only learned irrelevant details.
What do you think ?
When I try to introduce dropout, training become incredibly slow with bad acc.
If I try image augmentation, results are more promising but of course much slower.
I wonder what to look next:
- try running more epochs on the image augmented model
- try some medical pretrained model (do you know where to look)
What would you use as parameters for image augmentation (preferably in Keras) with Xray images ?
tensorflow keras computer-vision medical
tensorflow keras computer-vision medical
asked Nov 11 at 8:50
S Dufour
152
asked Nov 11 at 8:50
S Dufour
152
asked Nov 11 at 8:50
S Dufour
152
asked Nov 11 at 8:50
S Dufour
152
asked Nov 11 at 8:50
S Dufour
152
152
To establish, how many images are you using for training and test data? There are many reasons why the validation accuracy may not be rising, but would be good to know how many images you are training on, as well as what types of x-ray images you are working with.
– Michael Grogan
Nov 11 at 15:09
Hi, I have 40k images in training and 3.6k in validation with 60.8% of negative examples. Those are Musculoskeletal Radiographs of Elbow, Finger, Forearm and Humerus. They are available in RGB but all the channels are equal.
– S Dufour
Nov 11 at 15:37
While health itself is not my expertise, have you accounted for voxel spacing? Essentially, since you are working with health data, it is likely that you would need to implement spatial normalisation, since this would allow the algorithm to better classify each image while accounting for voxel spacing. This article might provide you with more insights: medium.com/tensorflow/…
– Michael Grogan
Nov 14 at 15:18
add a comment |
To establish, how many images are you using for training and test data? There are many reasons why the validation accuracy may not be rising, but would be good to know how many images you are training on, as well as what types of x-ray images you are working with.
– Michael Grogan
Nov 11 at 15:09
Hi, I have 40k images in training and 3.6k in validation with 60.8% of negative examples. Those are Musculoskeletal Radiographs of Elbow, Finger, Forearm and Humerus. They are available in RGB but all the channels are equal.
– S Dufour
Nov 11 at 15:37
While health itself is not my expertise, have you accounted for voxel spacing? Essentially, since you are working with health data, it is likely that you would need to implement spatial normalisation, since this would allow the algorithm to better classify each image while accounting for voxel spacing. This article might provide you with more insights: medium.com/tensorflow/…
– Michael Grogan
Nov 14 at 15:18
To establish, how many images are you using for training and test data? There are many reasons why the validation accuracy may not be rising, but would be good to know how many images you are training on, as well as what types of x-ray images you are working with.
– Michael Grogan
Nov 11 at 15:09
To establish, how many images are you using for training and test data? There are many reasons why the validation accuracy may not be rising, but would be good to know how many images you are training on, as well as what types of x-ray images you are working with.
– Michael Grogan
Nov 11 at 15:09
Hi, I have 40k images in training and 3.6k in validation with 60.8% of negative examples. Those are Musculoskeletal Radiographs of Elbow, Finger, Forearm and Humerus. They are available in RGB but all the channels are equal.
– S Dufour
Nov 11 at 15:37
Hi, I have 40k images in training and 3.6k in validation with 60.8% of negative examples. Those are Musculoskeletal Radiographs of Elbow, Finger, Forearm and Humerus. They are available in RGB but all the channels are equal.
– S Dufour
Nov 11 at 15:37
While health itself is not my expertise, have you accounted for voxel spacing? Essentially, since you are working with health data, it is likely that you would need to implement spatial normalisation, since this would allow the algorithm to better classify each image while accounting for voxel spacing. This article might provide you with more insights: medium.com/tensorflow/…
– Michael Grogan
Nov 14 at 15:18
While health itself is not my expertise, have you accounted for voxel spacing? Essentially, since you are working with health data, it is likely that you would need to implement spatial normalisation, since this would allow the algorithm to better classify each image while accounting for voxel spacing. This article might provide you with more insights: medium.com/tensorflow/…
– Michael Grogan
Nov 14 at 15:18
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To establish, how many images are you using for training and test data? There are many reasons why the validation accuracy may not be rising, but would be good to know how many images you are training on, as well as what types of x-ray images you are working with.
– Michael Grogan
Nov 11 at 15:09
Hi, I have 40k images in training and 3.6k in validation with 60.8% of negative examples. Those are Musculoskeletal Radiographs of Elbow, Finger, Forearm and Humerus. They are available in RGB but all the channels are equal.
– S Dufour
Nov 11 at 15:37
While health itself is not my expertise, have you accounted for voxel spacing? Essentially, since you are working with health data, it is likely that you would need to implement spatial normalisation, since this would allow the algorithm to better classify each image while accounting for voxel spacing. This article might provide you with more insights: medium.com/tensorflow/…
– Michael Grogan
Nov 14 at 15:18