I am working on Kaggle competition and trying to solve a multilabel classification problem with keras.
My dataset is highly imbalanced. I am familiar with this concept and did it for simple machine learning datasets, but now sure how to deal with both images and csv data.
There are a couple of questions, but they did not help me.
Use SMOTE to oversample image data
How to oversample image dataset using Python?
Class
No finding 25462
Aortic enlargement 5738
Cardiomegaly 4345
Pleural thickening 3866
Pulmonary fibrosis 3726
Nodule/Mass 2085
Pleural effusion 1970
Lung Opacity 1949
Other lesion 1771
Infiltration 997
ILD 792
Calcification 775
Consolidation 441
Atelectasis 229
Pneumothorax 185
I am trying to do oversampling, but not sure how to approach it. I have 15000 png images and train.csv dataset, which looks like:
image_id class_name class_id rad_id x_min y_min x_max y_max width height
0 50a418190bc3fb1ef1633bf9678929b3 No finding 14 R11 0.0 0.0 0.0 0.0 2332 2580
1 21a10246a5ec7af151081d0cd6d65dc9 No finding 14 R7 0.0 0.0 0.0 0.0 2954 3159
2 9a5094b2563a1ef3ff50dc5c7ff71345 Cardiomegaly 3 R10 691.0 1375.0 1653.0 1831.0 2080 2336
3 051132a778e61a86eb147c7c6f564dfe Aortic enlargement 0 R10 1264.0 743.0 1611.0 1019.0 2304 2880
4 063319de25ce7edb9b1c6b8881290140 No finding 14 R10 0.0 0.0 0.0 0.0 2540 3072
How to attack this problem, when I have images and csv?
When I converted data, it looks like:
Images Class
56 d106ec9b305178f3da060efe3191499a.png Nodule/Mass
38694 081d1700020b6bf0099f1e4d8aeec0f3.png Lung Opacity
50141 ff8ef73390f04480aba0be7810ef94cf.png No finding
233 253d35b7096d0957bd79cfb4b1c954e1.png No finding
2166 1951e0eba7c68aa1fbd6d723f19ee7c4.png Pleural thickening
I use image generator
# Create a train generator
train_generator = train_dataGen.flow_from_dataframe(dataframe = train,
directory = 'my_directory',
x_col = 'Images',
y_col = 'Class',
class_mode = 'categorical',
# target_size = (256, 256),
batch_size = 32)
I tried something dumb, but obviously did not work.
# Create an instance
oversample = SMOTE()
# Oversample
train_ovsm, valid_ovsm = oversample.fit_resample(train_ovsm, valid_ovsm)
Gives me an error:
ValueError: could not convert string to float: '954984f75efe6890cfa45d0784a3a1e6.png'
Appreciate tips and good tutorials, cannot find anything so far.
I'm not sure if this answer satisfies you or not, but here is my thought. If I were you, I wouldn't try to balance it in the way you're trying it now. IMO, that's not the proper way. Your main concern is this VinBigData is highly imbalanced and you're not sure how to address it properly.
Here are some first approaches all would adopt to address this issue in this competition.
- External dataset
- Heavy and meaningful augmentation
- Modified the loss function
What you need to do, collect all possible external samples from these datasets, combine them and make new datasets. It may take time but it worth it.
We all know augmentation is one of the key strategies for deep learning model training. But it would make sense to choose the right augmentation. Here are some demonstrations. The main intuition is to try not to destroy sensitive information. Be careful on that.
You can modify the loss function to weight the predicted score. Here is a detailed explanation of this topic.