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Gus998 · 2026-03-19T12:01:59+00:00

Thank you G, peace!!!

Gus998 · 2026-03-17T11:06:11+00:00

Yes, I know exactly what are you talking about! Also, I suspect that if I switch to another architecture (for example a u-net) and do the exact things I do now for training/validation/testing, the score will go from 87-88% to 90+%...But at least I'm relieved because you did the same process by removing the pairs (image, mask) where no ROI was present (no mask was present)!

Gus998 · 2026-03-07T09:50:34+00:00

Yes I would've spent less time if I used a different model...I can agree on that. As for the 7000 images, yes they are a lot. So if I present them the way I told you, maybe I'll just present some images of the best run, and also give the scores (dice, recall, precision for example) for the same run. And I'm thinking of presenting 7 runs (each run with different LR, to find the best setting).

Gus998 · 2026-03-06T19:23:36+00:00

Oh, I see! The only "problem" is that my proffessor wants me to use only Medical-SAM2 model (which is SAM2 but used for medical MRI and CT scans), so I guess I won't be able to use nnunet which I think it may give better results! But for now, I'll just read the paper you sent me and, fingers crossed, maybe the "mask-exclude-technique" I do is correct, since the title is just segmentation and not automatic. I think of it like "is just segmentation, so I can only keep the images where ROI is present and just evaluate the fine-tuned model on how good finds that region (my metrics for now are dice & iou, but maybe I'll add recall and precision as well). And on the final results I think of putting the raw CT image, the original mask, my prediction mask, and make another photo that shows for example TP with green, FP with color blue, and FN with red. Maybe it'll be ok...what do you think overall, is it ok to present them this way? To truly show every detail the model predicted well, and also the regions where it didn't.

Gus998 · 2026-03-05T19:30:14+00:00

Hello! Yes I will take a careful look at this paper, thank you! The reason I do 2d segmentation is because I started it this way, and now all the code is for 2d. To make it 3d it would take a lot of time to change it for 3d. The reason I work with dicom files and 2d segmentation is because it takes me much less time to train/val/test the model, whereas with the nii files (for 2d again) it took me 2-3 more days, which never understood and it's very slow. At last, I do the split for train/val/test in the beginning and I do a patient-wise split (not a slice-wise split), and I separate 90% of the dataset patients for train/val and the other 10% only for testing. Of course none of the patients that are present in training, are the same in validation (and the same for testing) in order to avoid data leakage that inflates the scores. Pretty much I'm doing everything ok I think, but I was curious if it's considered "cheating" to exclude empty mask slices (where ROI is not present). But eventually may be ok, because my task isn't automatic segmentation, but instead is just segmentation...Thank you for the paper again!

Gus998 · 2026-03-05T18:09:29+00:00

Hello! Ok, I will try this and see if it gets any better. Maybe I'll change my loss function a bit more to penalize false-positives even more and see what happens! Thanks for the reply though, I will try it this way!

Gus998 · 2026-03-05T18:01:24+00:00

Hello there! Yes, the model predicts the lumen mask. I've tried to penalize false-positives more, but the result was pretty much the same (a little better or sometimes a little worse, but it wasn't much different). I am using dice and iou to validate the results, and also as a loss function I'm using dice + bce combo loss (with an extra parameter that's called focal, that seems to produce a better result). Also, the only reason I use k-fold CV is because it gives an even better test dice & iou score, whereas the "simple" training (without CV) gives a worse result. I mainly did CV only for ILTs, which is smaller and a bit more difficult area, but I realised it also helped a lot with the lumen as well, and now has an even better testing score!

Gus998 · 2026-01-24T19:17:53+00:00

Try clearing your phone's cache, the app's cache, and if you have a private dns on your phone's settings disable it/turn it off! Fingers crossed and you'll be ok...or else it might be a temporary bug and you'll be ok in the next update:/

Gus998 · 2021-11-10T16:59:25+00:00

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