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KidneySeg-UNet
kssrikar4/KidneySeg-UNet
KidneySeg-UNet is a machine learning model from kssrikar4. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mpl-2.0.
[](https://python.org/) [](https://pytorch.org/) [](https://monai.io/) [](LICENSE)
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Updated Jun 1, 2026
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At a glance
- License
- mpl-2.0
- Access
- Public
- Created
- Jun 1, 2026
- Updated
- Jun 1, 2026
- SHA
- 8149034a
Model card
From the Hugging Face model README
KiTS Kidney Tumor Segmentation
Two-stage segmentation pipeline for kidney and tumor delineation in abdominal CT scans, optimized for the KiTS Challenge. Combines coarse volumetric localization with high-resolution refinement using MONAI's SegResNet.
Architecture
| Stage | Purpose | Input | Output |
|---|---|---|---|
| 1 (Localization) | Coarse kidney ROI extraction | 128³ downsampled volume | Binary kidney mask |
| 2 (Refinement) | Fine-grained kidney + tumor segmentation | Native-res crop + Stage 1 mask (3 channels) | Multi-class segmentation (kidney/tumor) |
Training Configuration
- Loss:
DiceFocalLoss(γ=2.0) – mitigates class imbalance, emphasizes boundary accuracy - Optimizer:
AdamW+CosineAnnealingLR - Augmentations:
RandRotated,RandFlipd,RandGaussianNoise,RandGaussianSmooth,RandScaleIntensity,RandShiftIntensity - Framework: PyTorch + MONAI
Results
Evaluated on held-out test set (Dice similarity coefficient):
| Stage | Best Epoch | Dice Score |
|---|---|---|
| 1 (Localization) | 66 | 0.87 |
| 2 (Refinement) | 110 | 0.66 |
Note: Stage 2 operates on high-resolution, tumor-sparse crops cause boundary precision is critical. The lower Dice reflects the inherent difficulty of fine-grained tumor delineation—not model failure.
Results on Test Set
Code & Usage
The full training and inference code, along with a Streamlit‑based graphical user interface for running the model on your own CT/CBCT volumes, is available in the GitHub repository.
License
Mozilla Public License Version 2.0 - Feel free to use and modify
- License
- mpl-2.0
- Created
- Jun 1, 2026
- Updated
- Jun 1, 2026