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jantonio78 OP t1_iu6hq97 wrote

Reply to comment by Yeinstein20 in Binary segmentation with imbalanced data by jantonio78

The images are grayscale x-ray images that have masses in them. For example, one of the images may have a shape of (2000, 1500, 1). I extract chunks of (32, 32, 1) and use those chunks to train the segmentation network. The dice score and loss is calculated on the foreground, and there is only one class (mass). I'm going to change the data loader to use only chunks with at least some mass in them, although my guess is that then the trained model is going to find masses in empty chunks too. Thanks for your suggestions!

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Yeinstein20 t1_iu80u42 wrote

Is there a particular reason why you chose a patch size of 32x32? You have a rather shallow U-Net with this small patch size and not that much information in your patches but look rather uniform. I would try to go for maybe 128x128 or even 256x256. For 2d segmentation that should still not be too memory intensive. What's the batch size you are using? If you use for example a batch size of 32 you could force in the data loader that at least 8 of the scans should have some mass in them. Just play around a bit with this number to see how it works. Keep an eye on recall and precision in addition to dice to see if your false positives will rise.

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jantonio78 OP t1_iudv7jg wrote

No particular reason. I'm going to try different patch sizes. Regarding the batch size, right now I'm using 32. Discarding chunks without mass, I get a dice score of 0.8 approx. which isn't really great, but I still have many things to try. And I'm checking recall and precision (and specificity) at the end of each epoch.

I'm going to try a bigger patch size and change the data loader as you suggested. Thanks for your help!

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