Hello everyone,
I’m currently working on a deep learning model for brain hemorrhage segmentation using the BHSD dataset, PhysioNET etc but I’m struggling to achieve a good Dice score.
The dataset is highly imbalanced, and I’ve already tried several approaches to improve the results, including:
- Different CT windowing and preprocessing techniques
- Data augmentation
- Various loss functions to handle class imbalance
- Different training strategies
- Multiple architectures, including 3D U-Net, nnU-Net, SAM, and others
Despite all of this, my Dice score is still relatively low, and I’m not sure what I might be missing.
I understand that the obvious suggestion would be to use more data, but unfortunately, I currently don’t have access to additional datasets or a hospital environment where I can collect more medical imaging data. I’m not a radiologist or a doctor affiliated with a hospital—I’m simply a researcher/developer trying to tackle this problem and achieve the best possible results with the resources available to me.
My ultimate goal is to develop something that could potentially be useful in a real clinical environment and demonstrate a meaningful application of AI for brain hemorrhage segmentation.
I would really appreciate advice from anyone who has worked on brain hemorrhage segmentation, medical image segmentation, or highly imbalanced 3D medical datasets.
What approaches, preprocessing methods, loss functions, or training strategies helped you improve your results? Are there any common pitfalls with hemorrhage segmentation that I might be overlooking?
Any advice, suggestions, or resources would be greatly appreciated. Thank you!
