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README.md
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license: apache-2.0
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---
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```
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wget https://huggingface.co/qicq1c/DiffTumor/difftumor_final.sif
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```
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```
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SINGULARITYENV_CUDA_VISIBLE_DEVICES=0 singularity run --nv -B $inputs_data:/workspace/inputs -B $outputs_data:/workspace/outputs difftumor_final.sif
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```
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license: apache-2.0
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---
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# DiffTumor
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The Singularity container is designed for the prediction of abdominal tumors, specifically targeting the liver, pancreas, and kidneys. It utilizes a segmentation model sourced from DiffTumor. [DiffTumor](https://github.com/MrGiovanni/DiffTumor).
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# Instructions
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### 1-Download
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Download the singularity container.
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```
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wget https://huggingface.co/qicq1c/DiffTumor/difftumor_final.sif
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```
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### 2-Data preparation
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This is how `inputs_data` organizes
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```
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$inputs_data/
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βββ case00001.nii.gz
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βββ case00002.nii.gz
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βββ case00003.nii.gz
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βββ case000xx.nii.gz
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βββ ...
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βββ case10000.nii.gz
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```
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### 3-Inference
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You can directly perform inference on your own data. Simply modify inputs_data to reflect your data path and adjust outputs_data to specify the desired output location for the segmentation results.
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```
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SINGULARITYENV_CUDA_VISIBLE_DEVICES=0 singularity run --nv -B $inputs_data:/workspace/inputs -B $outputs_data:/workspace/outputs difftumor_final.sif
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```
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This is how `outputs_data` organizes
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```
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$outputs_data/
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βββ case00001
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βββ case00002
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βββ case00003
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βββ ct.nii.gz
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βββ predictions
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βββ liver.nii.gz
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βββ pancreas.nii.gz
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βββ kidney.nii.gz
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βββ liver_tumor.nii.gz
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βββ pancreas_tumor.nii.gz
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βββ kidney_tumor.nii.gz
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```
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