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lymph_node_segmentation

Image Segmentation
Transformers
Safetensors
PyTorch
segmentation
multilabel
unet
medical-imaging
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@@ -37,16 +37,29 @@ A unet model for multilabel image segmentation trained with sliding window appro
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  | Patience | 10 |
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  | Dataset | GleghornLab/Semi-Automated_LN_Segmentation_10_11_2025 |
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- ## Test Performance Metrics
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-
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- | Metric | Mean | Class 0 | Class 1 | Class 2 | Class 3 |
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- |--------|------|--------|--------|--------|--------|
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- | Dice | 0.7781 | 0.7197 | 0.7614 | 0.7271 | 0.9040 |
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- | IoU | 0.6524 | 0.5708 | 0.6199 | 0.5929 | 0.8261 |
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- | F1 | 0.7781 | 0.7197 | 0.7614 | 0.7271 | 0.9040 |
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- | MCC | 0.7832 | 0.7308 | 0.7730 | 0.7440 | 0.8850 |
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- | ROC AUC | 0.9945 | 0.9945 | 0.9975 | 0.9917 | 0.9943 |
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- | PR AUC | 0.9076 | 0.8357 | 0.9108 | 0.9095 | 0.9743 |
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ## Usage
 
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  | Patience | 10 |
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  | Dataset | GleghornLab/Semi-Automated_LN_Segmentation_10_11_2025 |
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+
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+ ## Validation Performance Metrics (Pooled Pixel-Level)
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+
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+ | Metric | Mean | Class 0 | Class 1 | Class 2 | Class 3 |
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+ | ------------ | -----: | ------: | ------: | ------: | ------: |
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+ | Dice | 0.8697 | 0.7635 | 0.8607 | 0.9085 | 0.9461 |
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+ | IoU | 0.7757 | 0.6174 | 0.7554 | 0.8323 | 0.8977 |
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+ | F1 (Pooled) | 0.8697 | 0.7635 | 0.8607 | 0.9085 | 0.9461 |
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+ | MCC (Pooled) | 0.8649 | 0.7636 | 0.8588 | 0.9032 | 0.9340 |
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+ | ROC AUC | 0.9971 | 0.9946 | 0.9983 | 0.9978 | 0.9978 |
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+ | PR AUC | 0.9412 | 0.8527 | 0.9494 | 0.9720 | 0.9908 |
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+
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+
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+ ## Test Performance Metrics (Pooled Pixel-Level)
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+
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+ | Metric | Mean | Class 0 | Class 1 | Class 2 | Class 3 |
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+ | ------------ | -----: | ------: | ------: | ------: | ------: |
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+ | Dice | 0.7960 | 0.7578 | 0.7830 | 0.7344 | 0.9087 |
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+ | IoU | 0.6666 | 0.6101 | 0.6433 | 0.5803 | 0.8328 |
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+ | F1 (Pooled) | 0.7960 | 0.7578 | 0.7830 | 0.7344 | 0.9087 |
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+ | MCC (Pooled) | 0.7959 | 0.7609 | 0.7891 | 0.7436 | 0.8900 |
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+ | ROC AUC | 0.9942 | 0.9949 | 0.9975 | 0.9903 | 0.9941 |
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+ | PR AUC | 0.9117 | 0.8628 | 0.9133 | 0.8985 | 0.9720 |
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  ## Usage