Hello,

I am working on a project in which I'm detecting cavities in X-rays.

The dataset I have is pretty limited (~100 images). Each X-ray has a black and white mask that shows where in the image are the cavities.

I'm trying to improve my results.

What I've tried so far:

different loss functions: BCE, dice loss, bce+dice, tversky loss, focal tversky loss
modifying the images' gamma to make the cavities more visible
trying out different U-Nets: U-net, V-net, U-net++, UNET 3+, Attention U-net, R2U-net, ResUnet-a, U^2-Net, TransUNET, and Swin-UNET

None of the new U-nets that I've tried improved the results. Probably because they are more suited for a larger dataset.

I'm now looking for other things to try to improve my results. Currently my network is detecting cavities, but it has trouble with the smaller ones.

Comments

Seahorsejockey t1_je6m3oa wrote on March 29, 2023 at 7:56 PM

#2,458,853

How Big are your images (resolution HxW)?

viertys OP t1_je6oxpk wrote on March 29, 2023 at 8:14 PM

#2,459,443

Replying to Seahorsejockey (#2,458,853)

512x512, but I can modify their dimensions

cma_4204 t1_je79lg9 wrote on March 29, 2023 at 10:34 PM

#2,464,068

SMP has a lot of different choices for architecture other than unet, and a ton of different encoders. I like deeplabv3+/unet with regnety encoder, works well for most things https://github.com/qubvel/segmentation_models.pytorch

trajo123 t1_je79rme wrote on March 29, 2023 at 10:36 PM

#2,464,086

Have you tried using the segmentation models from the SMP package (Iakubovskii, P. (2019)? I built a segmentation model for dermoscopy images and pre-trained models consistently outperformed anything else, architecture didn't matter that much. I got best results with "U-Net with SegFormer pre-trained encoder".

It depends how much training data you have, but unless you have millions of samples, pre-training usually trumps architecture.

Environmental_Ice422 t1_je8e9cy wrote on March 30, 2023 at 3:52 AM

#2,472,986

Replying to cma_4204 (#2,464,068)

SMP seems to only support 2D image segmentation, but medical images are usually 3D.

Environmental_Ice422 t1_je8fe4r wrote on March 30, 2023 at 4:03 AM

#2,473,228

I would suggest you to apply more dramatic image augmentation methods

Yeinstein20 t1_je8trp8 wrote on March 30, 2023 at 6:48 AM

#2,476,276

Your dataset is rather small and it seems you are not really doing Augmentations? I would try different Augmentations, that should improve your results regardless of the used model. Have you looked at some frameworks for medical image segmentation? nnUNet comes to mind which would give you a solid baseline. How good are your results currently?

viertys OP t1_je9lv4x wrote on March 30, 2023 at 12:40 PM

#2,481,786

Replying to Environmental_Ice422 (#2,473,228)

I am currently using the albumentations module. I rotate, shift, rotate, blur, horizontal flip, downscale and use gauss noise. I get around 400 images after doing this. Is there anything you would suggest?

viertys OP t1_je9m4ao wrote on March 30, 2023 at 12:42 PM

#2,481,845

Replying to Yeinstein20 (#2,476,276)

I didn't mention it in the post, but I'm using the albumentations module. I rotate, shift, rotate, blur, horizontal flip, downscale and use gauss noise. I get around 400 images after doing this. Is there anything you would suggest?

I have an accuracy of 98.50 and I have dice of around 0.30-0.65 in each image

viertys OP t1_je9m705 wrote on March 30, 2023 at 12:43 PM

#2,481,866

Replying to trajo123 (#2,464,086)

Thank you! I will try

Environmental_Ice422 t1_jed1cqb wrote on March 31, 2023 at 3:00 AM

#2,513,398

Replying to viertys (#2,481,786)

You should apply those transforms on each batch while training, rather than transforming the data before training. This approach is called doing the augmentation on the fly.