File size: 10,128 Bytes

---
license: mit
tags:
  - medical-imaging
  - chest-x-ray
  - insurance-prediction
  - fairness
  - bias-detection
  - mimic-cxr
datasets:
  - mimic-cxr
---

# Insurance Paper — Pretrained Weights

Pretrained checkpoints for **"The Unawareness of AI Looking for Health Insurance Type from Normal Chest X-ray Images"**.

All models were trained with seed=123 on the MedGemma-refined subset of [MIMIC-CXR-JPG v2.0.0](https://physionet.org/content/mimic-cxr-jpg/2.0.0/) (`mimic-cxr-gemma`), which contains only normal CXRs filtered by MedGemma. Some baseline and subgroup experiments also include CheXpert checkpoints.

## Models

| Model | Tag in file/folder names | Architecture | File size |
|-------|--------------------------|--------------|-----------|
| DenseNet121 | `densenet` | DenseNetWithDoubleLinear / _addDemothen2 | ~59 MB |
| MedMamba | `mamba` | VSSM_DoubleLinear / VSSM_Double_addDemothen2 | ~115 MB |
| MedMamba-S (pretrained) | `mamba_pretrained`, `*_s_fix` folders | VSSM (small), initialised from `MedMamba_pretrained_weight/MedMamba_S_PneumoniaMNIST.pth` | ~146 MB |
| Swin Transformer V2 | `swinTF` | SwinTDoubleLinear / _addDemothen2 | ~218 MB |
| Swin Transformer (pretrained, Base) | `swinTFB`, `PretrainedB`, `pretrainedB` | SwinTDoubleLinear / _addDemothen2 | ~690 MB |

## Repository Structure

```
insurance_paper_weights/
├── MedMamba_pretrained_weight/          # MedMamba initialisation weights
│   └── MedMamba_PneumoniaMNIST.pth, MedMamba_S_PneumoniaMNIST.pth
│
├── exp0_medgemma/                       # Baselines (CheXpert / MIMIC)
│   ├── MedGemma_CheXpert_{densenet,mamba,mamba_pretrained,swinTF,swinTF_pretrainedB}.pt
│   └── MedGemma_MIMIC_{densenet_0228,mamba_0228,mamba_pretrained,swinTF_0228,swinTF_pretrainedB}.pt
│
├── exp0-2_medgemma/                     # Random init / perturbed labels
│   ├── {densenet,mamba,mamba_pretrained,swinTF,swinTF_pretrained}_random.pt
│   ├── {densenet,mamba_pretrained,swinTF_pretrained}_perturbed[_chexpert].pt
│   └── {densenet,mamba_pretrained,swinTF_pretrained}_patientlevelperturbed[_chexpert].pt
│
├── exp1_medgemma/                       # Patch keep/remove — DenseNet
│   ├── densenet_keep/    └── patch1.pt ... patch9.pt
│   └── densenet_remove/  └── patch1.pt ... patch9.pt
│
├── exp1-1_medgemma_s_fix/               # Patch keep/remove — MedMamba-S (pretrained)
│   ├── mamba_keep/       └── patch1.pt ... patch9.pt
│   └── mamba_remove/     └── patch1.pt ... patch9.pt
│
├── exp1-2_medgemma/                     # Patch keep/remove — Swin Transformer
│   ├── swinTF_keep/      └── patch1.pt ... patch9.pt
│   ├── swinTF_remove/    └── patch1.pt ... patch9.pt
│   ├── swinTFB_keep/     └── SwinTFB_patch1.pt ... SwinTFB_patch9.pt
│   └── swinTFB_remove/   └── SwinTFB_patch1.pt ... SwinTFB_patch9.pt
│
├── exp2_medgemma_s_fix/                 # Resolution — MedMamba-S (pretrained)
│   └── mamba_2.pt ... mamba_224.pt (8 files)
│
├── exp2-1_medgemma/                     # Resolution — DenseNet
│   └── densenet_2.pt ... densenet_224.pt (8 files)
│
├── exp2-2_medgemma/                     # Resolution — Swin Transformer (pretrained Base)
│   └── PretrainedSwinTFB_2.pt ... PretrainedSwinTFB_224.pt (8 files)
│
├── exp3_medgemma_s_fix/                 # Demographics — MedMamba-S (pretrained)
│   └── sex.pt, age.pt, race.pt, sexage.pt, sexrace.pt, agerace.pt, sexagerace.pt
│
├── exp3-1_medgemma/                     # Demographics — DenseNet
│   └── (same 7 files)
│
├── exp3-2_medgemma/                     # Demographics — Swin Transformer
│   ├── {combo}.pt                       (7 files)
│   └── {combo}_PretrainedB_swinTF.pt    (7 files)
│
├── exp4_medgemma_s_fix/                 # Frequency filtering — MedMamba-S (pretrained)
│   ├── highpass/  └── 1Hz.pt ... 400Hz.pt
│   └── lowpass/   └── 1Hz.pt ... 400Hz.pt
│
├── exp4-1_medgemma/                     # Frequency filtering — Swin Transformer
│   ├── highpass/  └── {F}Hz.pt, {F}HighPass_PretrainedB_swinTF.pt
│   └── lowpass/   └── {F}Hz.pt, {F}LowPass_PretrainedB_swinTF.pt
│
├── exp4-2_medgemma/                     # Frequency filtering — DenseNet
│   ├── highpass/  └── 1Hz.pt ... 400Hz.pt
│   └── lowpass/   └── 1Hz.pt ... 400Hz.pt
│
└── exp5_medgemma/                       # Single-subgroup training
    └── Only{Female,Male,Old,White}_{model}[_CheXpert]/Only{...}_{model}[_CheXpert].pt
```

## Checkpoint Table

| Experiment | Description | Model(s) | Config | # Checkpoints | Path |
|------------|-------------|----------|--------|---------------|------|
| **exp0** | Baselines | DenseNet, MedMamba, MedMamba-S, SwinT, SwinT-B | 5 models x 2 datasets | 10 | `exp0_medgemma/MedGemma_{CheXpert,MIMIC}_{model}.pt` |
| **exp0-2** | Random init / perturbed labels | DenseNet, MedMamba, MedMamba-S, SwinT, SwinT-B | random, perturbed, patient-level perturbed (MIMIC + CheXpert) | 17 | `exp0-2_medgemma/{model}_{setting}.pt` |
| **exp1** | Patch keep/remove | DenseNet | 9 patches x 2 | 18 | `exp1_medgemma/densenet_{keep,remove}/patch{N}.pt` |
| **exp1-1** | Patch keep/remove | MedMamba-S | 9 patches x 2 | 18 | `exp1-1_medgemma_s_fix/mamba_{keep,remove}/patch{N}.pt` |
| **exp1-2** | Patch keep/remove | SwinT, SwinT-B | 9 patches x 2 x 2 models | 36 | `exp1-2_medgemma/{swinTF,swinTFB}_{keep,remove}/...` |
| **exp2** | Resolution | MedMamba-S | 8 resolutions | 8 | `exp2_medgemma_s_fix/mamba_{N}.pt` |
| **exp2-1** | Resolution | DenseNet | 8 resolutions | 8 | `exp2-1_medgemma/densenet_{N}.pt` |
| **exp2-2** | Resolution | SwinT-B | 8 resolutions | 8 | `exp2-2_medgemma/PretrainedSwinTFB_{N}.pt` |
| **exp3** | Demographics (addDemo) | MedMamba-S | 7 demo combos | 7 | `exp3_medgemma_s_fix/{combo}.pt` |
| **exp3-1** | Demographics (addDemo) | DenseNet | 7 demo combos | 7 | `exp3-1_medgemma/{combo}.pt` |
| **exp3-2** | Demographics (addDemo) | SwinT, SwinT-B | 7 demo combos x 2 | 14 | `exp3-2_medgemma/{combo}[_PretrainedB_swinTF].pt` |
| **exp4** | Freq filtering (HP+LP) | MedMamba-S | 8 freq x 2 | 16 | `exp4_medgemma_s_fix/{highpass,lowpass}/{F}Hz.pt` |
| **exp4-1** | Freq filtering (HP+LP) | SwinT, SwinT-B | 8 freq x 2 x 2 | 32 | `exp4-1_medgemma/{highpass,lowpass}/...` |
| **exp4-2** | Freq filtering (HP+LP) | DenseNet | 8 freq x 2 | 16 | `exp4-2_medgemma/{highpass,lowpass}/{F}Hz.pt` |
| **exp5** | Single-subgroup training | DenseNet, MedMamba, MedMamba-S, SwinT, SwinT-B | 4 subgroups x 5 models x 2 datasets | 40 | `exp5_medgemma/Only{Group}_{model}[_CheXpert]/...` |

**Total: 255 checkpoints** (+ 2 MedMamba initialisation weights in `MedMamba_pretrained_weight/`)

- **Demo combos** (exp3): `sex`, `age`, `race`, `sexage`, `sexrace`, `agerace`, `sexagerace`
- **Frequencies** (exp4): 1, 5, 10, 25, 50, 100, 200, 400 Hz
- **Resolutions** (exp2): 2, 4, 7, 14, 28, 56, 112, 224
- **Patches** (exp1): 1-9, corresponding to a 3x3 grid on 448x448 images (left-to-right, top-to-bottom)
- **Subgroups** (exp5): `OnlyFemale`, `OnlyMale`, `OnlyOld`, `OnlyWhite`

## Usage

### Download all weights

```bash
git lfs install
git clone https://huggingface.co/InsurancePrediction/insurance_paper_weights
```

### Download a single experiment

```python
from huggingface_hub import snapshot_download
snapshot_download(
    repo_id="InsurancePrediction/insurance_paper_weights",
    allow_patterns="exp3-1_medgemma/*",
    local_dir="./weights"
)
```

### Load a checkpoint

```python
import torch

# Base model (exp1, exp2, exp4)
from model import DenseNetWithDoubleLinear
model = DenseNetWithDoubleLinear(num_classes=2, dropout_prob=0)
ckpt = torch.load("exp1_medgemma/densenet_keep/patch1.pt", map_location="cpu")
model.load_state_dict(ckpt["model_state_dict"])

# Demographics model (exp3)
from model import DenseNetWithDoubleLinear_addDemothen2
model = DenseNetWithDoubleLinear_addDemothen2(num_classes=2, demo_size=5)  # e.g. sexage -> 2+3=5
ckpt = torch.load("exp3-1_medgemma/sexage.pt", map_location="cpu")
model.load_state_dict(ckpt["model_state_dict"])
```

### Mapping from HF paths to original training paths

| HF path | Original training path |
|---------|----------------------|
| `exp2-1_medgemma/densenet_{N}.pt` | `JAMA_codes_medgemma/{N}_mg_exp2/densenet/Rand123/...` |
| `exp3-1_medgemma/{combo}.pt` | `JAMA_codes_medgemma/{combo}_mg_exp3-1/densenet/Rand123/...` |
| `exp3-2_medgemma/{combo}.pt` | `JAMA_codes_medgemma/{combo}_mg_exp3-2/swinTF/Rand123/...` |
| `exp4-1_medgemma/{highpass,lowpass}/{F}Hz.pt` | `JAMA_codes_medgemma/{F}{HighPass,LowPass}_mg_exp4-1/swinTF/Rand123/...` |
| `exp4-2_medgemma/{highpass,lowpass}/{F}Hz.pt` | `JAMA_codes_medgemma/{F}{HighPass,LowPass}_mg_exp4-2/densenet/Rand123/...` |

Bootstrap evaluation results: `bootstrap_results/` in the [code repository](https://github.com/altis5526/Insurance-Prediction-from-Normal-CXRs-Public-Version).

## Training Details

- **Dataset**: mimic-cxr-gemma (MedGemma-refined normal frontal CXRs from MIMIC-CXR-JPG v2.0.0); CheXpert for `*_CheXpert` / `*_chexpert` checkpoints
- **Task**: Binary classification — Private vs. Public/Government insurance
- **Image size**: 448 x 448
- **Seed**: 123
- **Selection**: Best validation AUC checkpoint

## Citation

```bibtex
@article{chen2025algorithms,
  title={Algorithms Trained on Normal Chest X-rays Can Predict Health Insurance Types},
  author={Chen, Chi-Yu and Abulibdeh, Rawan and Asgari, Arash and Ord{\'o}{\~n}ez, Sebasti{\'a}n Andr{\'e}s Cajas and Celi, Leo Anthony and Goode, Deirdre and Hamidi, Hassan and Seyyed-Kalantari, Laleh and McCague, Ned and Sounack, Thomas and others},
  journal={arXiv preprint arXiv:2511.11030},
  year={2025}
}
```

## Code

[Code Repository](https://github.com/altis5526/Insurance-Prediction-from-Normal-CXRs-Public-Version)