Datasets:
File size: 2,420 Bytes
51b5866 fe96f4d 51b5866 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 | # AbdCTBench Code
Training and evaluation pipeline for comorbidity prediction from abdominal CT scans.
## What This Code Does
- Train single-task or multi-task models from CSV + PNG data
- Evaluate checkpoints with reproducible metrics
- Load released checkpoints in `.safetensors` or `.pth` format
## Install
```bash
pip install -r requirements.txt
```
## Data Layout
Pass `--data_dir` pointing to:
```text
data_dir/
├── train.csv
├── val.csv
├── test.csv
└── data/
├── <FILE>.png
└── ...
```
`FILE` values in CSV must match PNG basenames.
## Train
```bash
python train.py \
--model "ResNet-18" \
--data_dir ../AbdCTBench_dataset \
--biomarker_config ./config/biomarker_config_multitask_example.yaml \
--output_dir ./outputs
```
Reproducibility flags:
- `--seed 42`
- `--deterministic`
## Evaluate
```bash
python test.py \
--data_dir ../AbdCTBench_dataset \
--checkpoint_path ../models/mi_only/ResNet-18_lr1e-05_bs16/best_checkpoint.safetensors \
--biomarker_config ../models/mi_only/ResNet-18_lr1e-05_bs16/biomarker_config.json \
--output_dir ./test_results
```
Useful options:
- `--save_predictions`
- `--save_metrics`
- `--only_pred`
## Checkpoint Folder Contents
Each released model folder contains:
- `best_checkpoint.safetensors`
- `config.json`
- `biomarker_config.json`
## DICOM to PNG Pipeline
The DICOM -> STL -> PNG conversion scripts are in:
- `dicom_stl_png_pipeline/`
Key files:
- `dicom2stl.py`: converts a DICOM series (folder/zip) to STL
- `stl2png_centered.py`: renders STL to a centered PNG
- `parseargs.py` and `utils/`: argument parsing and volume/mesh helper utilities
- `requirements.txt` in this folder: extra dependencies for this conversion pipeline
Install pipeline dependencies:
```bash
pip install -r dicom_stl_png_pipeline/requirements.txt
```
Minimal usage:
```bash
python dicom_stl_png_pipeline/dicom2stl.py \
--type skin \
--output ./sample.stl \
/path/to/dicom_series_folder
```
```bash
python dicom_stl_png_pipeline/stl2png_centered.py \
./sample.stl \
./sample.png
```
Notes:
- This conversion pipeline is optional and separate from model train/test.
- `train.py` and `test.py` consume PNG + CSV data and do not run DICOM conversion internally.
## Biomarker Config Templates
- `config/biomarker_config_single_task_example.yaml`
- `config/biomarker_config_multitask_example.yaml`
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