VivaKitBrain Tumour MRI: Segmentation & Classification
A multi-task U-Net that outlines the tumour and names its type (meningioma, glioma, pituitary) in one pass. Learn Dice loss, skip connections, paired augmentation and why splitting by patient matters.
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Our honest results
| Mean Dice (457 test slices, 34 unseen patients) | 0.794 |
| Median Dice | 0.885 |
| Tumour-type accuracy | 0.956 |
| Macro-F1 | 0.953 |
Glioma is the hard case (Dice 0.680 vs 0.931 for meningioma) - discussed with failure examples in the guide. Trained in 6.1 min on an RTX 5070 Laptop GPU.
What's included
- Readable source codeSmall, commented modules you can explain line by line in your viva.
- Demo appA local web app to show your examiners - runs offline on your laptop.
- Trained model + reference resultsSo you can compare your own runs fairly.
- Learning guide with viva Q&ABackground, design choices explained, limitations and extension ideas.
- One-click setup & testsWindows setup script and tests that prove it works on your PC.
- Dataset download scriptFetches the public data from its original source, with the citation.
Dataset credit
Cheng J. (2017). brain tumor dataset. figshare. doi:10.6084/m9.figshare.1512427 - CC BY 4.0.
Academic integrity
We sell code and teaching, not finished projects. You must understand, extend and write up the work yourself, acknowledge the kit and cite the dataset. No ready-made reports.