| ---
|
| license: mit
|
| tags:
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| - medical-imaging
|
| - chest-x-ray
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| - 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 on [MIMIC-CXR-JPG v2.0.0](https://physionet.org/content/mimic-cxr-jpg/2.0.0/) with seed=123. MedGemma experiments use the MedGemma-refined subset (`mimic-cxr-gemma`), containing only normal CXRs filtered by MedGemma.
|
|
|
| ## Models
|
|
|
| | Model | Architecture | Params | File size |
|
| |-------|-------------|--------|-----------|
|
| | MedMamba | VSSM_DoubleLinear / VSSM_Double_addDemothen2 | ~29M | ~115 MB |
|
| | DenseNet121 | DenseNetWithDoubleLinear / _addDemothen2 | ~8M | ~59 MB |
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| | Swin Transformer V2 | SwinTDoubleLinear / _addDemothen2 | ~50M | ~218 MB |
|
|
|
| ## Repository Structure
|
|
|
| ```
|
| insurance_paper_weights/
|
| ├── exp0/ # Baselines (CheXpert / MIMIC)
|
| │ ├── CheXpert/
|
| │ │ └── densenet.pt, mamba.pt, swinTF.pt
|
| │ └── MIMIC/
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| │ └── densenet.pt, mamba.pt, swinTF.pt
|
| │
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| ├── exp0-2/ # Random Initialization
|
| │ └── swinTF_random.pt
|
| │
|
| ├── exp1/ # Patch keep/remove — DenseNet
|
| │ ├── densenet_keep/
|
| │ │ └── patch1.pt ... patch9.pt
|
| │ └── densenet_remove/
|
| │ └── patch1.pt ... patch9.pt
|
| │
|
| ├── exp1-1/ # Patch keep/remove — MedMamba
|
| │ ├── mamba_keep/
|
| │ │ └── patch1.pt ... patch9.pt
|
| │ └── mamba_remove/
|
| │ └── patch1.pt ... patch9.pt
|
| │
|
| ├── exp1-2/ # Patch keep/remove — DenseNet + Swin Transformer
|
| │ ├── densenet_keep/
|
| │ │ └── patch1.pt ... patch9.pt
|
| │ ├── densenet_remove/
|
| │ │ └── patch1.pt ... patch9.pt
|
| │ ├── swinTF_keep/
|
| │ │ └── patch1.pt ... patch9.pt
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| │ └── swinTF_remove/
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| │ └── patch1.pt ... patch9.pt
|
| │
|
| ├── exp2/ # Resolution — MedMamba
|
| │ └── mamba_2.pt ... mamba_224.pt (8 files)
|
| │
|
| ├── exp2-1/ # Resolution — DenseNet
|
| │ └── densenet_2.pt ... densenet_224.pt (8 files)
|
| │
|
| ├── exp2-2/ # Resolution — Swin Transformer
|
| │ └── swinTF_4.pt ... swinTF_224.pt (7 files)
|
| │
|
| ├── exp3/ # Demographics — MedMamba
|
| │ ├── sex.pt, age.pt, race.pt
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| │ ├── sexage.pt, sexrace.pt, agerace.pt
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| │ └── sexagerace.pt
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| │
|
| ├── exp3-1/ # Demographics — DenseNet
|
| │ └── (same 7 files)
|
| │
|
| ├── exp3-2/ # Demographics — Swin Transformer
|
| │ └── (same 7 files)
|
| │
|
| ├── exp4/ # Frequency filtering — MedMamba
|
| │ ├── highpass/
|
| │ │ └── 1Hz.pt, 5Hz.pt, ... 400Hz.pt
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| │ └── lowpass/
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| │ └── 1Hz.pt, 5Hz.pt, ... 400Hz.pt
|
| │
|
| ├── exp4-1/ # Frequency filtering — Swin Transformer
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| │ ├── highpass/ └── lowpass/
|
| │
|
| ├── exp4-2/ # Frequency filtering — DenseNet
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| │ ├── highpass/ └── lowpass/
|
| │
|
| ├── exp2_medgemma/ # MedGemma Resolution — MedMamba
|
| │ └── mamba_2.pt ... mamba_224.pt (8 files)
|
| │
|
| ├── exp2-1_medgemma/ # MedGemma Resolution — DenseNet
|
| │ └── densenet_2.pt ... densenet_224.pt (8 files)
|
| │
|
| ├── exp2-2_medgemma/ # MedGemma Resolution — Swin Transformer
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| │ └── swinTF_4.pt ... swinTF_224.pt (7 files)
|
| │
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| ├── exp3_medgemma/ # MedGemma Demographics — MedMamba
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| │ ├── sex.pt, age.pt, race.pt
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| │ ├── sexage.pt, sexrace.pt, agerace.pt
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| │ └── sexagerace.pt
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| │
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| ├── exp3-1_medgemma/ # MedGemma Demographics — DenseNet
|
| │ └── (same 7 files)
|
| │
|
| ├── exp3-2_medgemma/ # MedGemma Demographics — Swin Transformer
|
| │ └── (same 7 files)
|
| │
|
| ├── exp4_medgemma/ # MedGemma Frequency filtering — MedMamba
|
| │ ├── highpass/
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| │ │ └── 1Hz.pt, 5Hz.pt, ... 400Hz.pt
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| │ └── lowpass/
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| │ └── 1Hz.pt, 5Hz.pt, ... 400Hz.pt
|
| │
|
| ├── exp4-1_medgemma/ # MedGemma Frequency filtering — Swin Transformer
|
| │ ├── highpass/ └── lowpass/
|
| │
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| └── exp4-2_medgemma/ # MedGemma Frequency filtering — DenseNet
|
| ├── highpass/ └── lowpass/
|
| ```
|
|
|
| ## Checkpoint Table
|
|
|
| > **Note:** All experiments are trained and available.
|
|
|
| | Experiment | Description | Model | Config | # Checkpoints | HF path | Available? |
|
| |------------|-------------|-------|--------|---------------|---------|------------|
|
| | **exp0** | Baselines (CheXpert/MIMIC) | All 3 | — | 6 | `exp0/{dataset}/{model}.pt` | Yes |
|
| | **exp0-2** | Random Initialisation | SwinT | — | 1 | `exp0-2/swinTF_random.pt` | Yes |
|
| | **exp1** | Patch keep/remove | DenseNet | 9 patches x 2 | 18 | `exp1/densenet_{keep/remove}/patch{N}.pt` | Yes |
|
| | **exp1-1** | Patch keep/remove | MedMamba | 9 patches x 2 | 18 | `exp1-1/mamba_{keep/remove}/patch{N}.pt` | Yes |
|
| | **exp1-2** | Patch keep/remove | SwinT | 9 patches x 2 | 18 | `exp1-2/swinTF_{keep/remove}/patch{N}.pt` | Yes |
|
| | **exp2** | Resolution | MedMamba | 8 resolutions | 8 | `exp2/mamba_{N}.pt` | Yes |
|
| | **exp2-1** | Resolution | DenseNet | 8 resolutions | 8 | `exp2-1/densenet_{N}.pt` | Yes |
|
| | **exp2-2** | Resolution | SwinT | 7 resolutions | 7 | `exp2-2/swinTF_{N}.pt` | Yes |
|
| | **exp3** | Demographics (addDemo) | MedMamba | 7 demo combos | 7 | `exp3/{combo}.pt` | Yes |
|
| | **exp3-1** | Demographics (addDemo) | DenseNet | 7 demo combos | 7 | `exp3-1/{combo}.pt` | Yes |
|
| | **exp3-2** | Demographics (addDemo) | SwinT | 7 demo combos | 7 | `exp3-2/{combo}.pt` | Yes |
|
| | **exp4** | Freq filtering (HP+LP) | MedMamba | 8 freq x 2 | 16 | `exp4/{highpass,lowpass}/{F}Hz.pt` | Yes |
|
| | **exp4-1** | Freq filtering (HP+LP) | SwinT | 8 freq x 2 | 16 | `exp4-1/{highpass,lowpass}/{F}Hz.pt` | Yes |
|
| | **exp4-2** | Freq filtering (HP+LP) | DenseNet | 8 freq x 2 | 16 | `exp4-2/{highpass,lowpass}/{F}Hz.pt` | Yes |
|
|
|
| **Original: 153 checkpoints**
|
|
|
| ### MedGemma Experiments (mimic-cxr-gemma dataset)
|
|
|
| | Experiment | Description | Model | Config | # Checkpoints | HF path | Available? |
|
| |------------|-------------|-------|--------|---------------|---------|------------|
|
| | **exp2** | Resolution | MedMamba | 8 resolutions | 8 | `exp2_medgemma/mamba_{N}.pt` | Yes |
|
| | **exp2-1** | Resolution | DenseNet | 8 resolutions | 8 | `exp2-1_medgemma/densenet_{N}.pt` | Yes |
|
| | **exp2-2** | Resolution | SwinT | 7 resolutions | 7 | `exp2-2_medgemma/swinTF_{N}.pt` | Yes |
|
| | **exp3** | Demographics (addDemo) | MedMamba | 7 demo combos | 7 | `exp3_medgemma/{combo}.pt` | Yes |
|
| | **exp3-1** | Demographics (addDemo) | DenseNet | 7 demo combos | 7 | `exp3-1_medgemma/{combo}.pt` | Yes |
|
| | **exp3-2** | Demographics (addDemo) | SwinT | 7 demo combos | 7 | `exp3-2_medgemma/{combo}.pt` | Yes |
|
| | **exp4** | Freq filtering (HP+LP) | MedMamba | 8 freq x 2 | 16 | `exp4_medgemma/{highpass,lowpass}/{F}Hz.pt` | Yes |
|
| | **exp4-1** | Freq filtering (HP+LP) | SwinT | 8 freq x 2 | 16 | `exp4-1_medgemma/{highpass,lowpass}/{F}Hz.pt` | Yes |
|
| | **exp4-2** | Freq filtering (HP+LP) | DenseNet | 8 freq x 2 | 16 | `exp4-2_medgemma/{highpass,lowpass}/{F}Hz.pt` | Yes |
|
|
|
| **MedGemma: 92 checkpoints**
|
|
|
| **Total: 153 original + 92 MedGemma = 245 unique checkpoints**
|
|
|
| - **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)
|
|
|
| ## Usage
|
|
|
| ### Download all weights
|
|
|
| ```bash
|
| git lfs install
|
| git clone https://huggingface.co/InsurancePrediction/insurance_paper_weights
|
| ```
|
|
|
| ### Download a single experiment
|
|
|
| ```bash
|
| # Using huggingface_hub
|
| from huggingface_hub import snapshot_download
|
| snapshot_download(
|
| repo_id="InsurancePrediction/insurance_paper_weights",
|
| allow_patterns="exp3-1/*",
|
| local_dir="./weights"
|
| )
|
| ```
|
|
|
| ### Load a checkpoint
|
|
|
| ```python
|
| import torch
|
|
|
| # Base model (exp1, exp4)
|
| from model import DenseNetWithDoubleLinear
|
| model = DenseNetWithDoubleLinear(num_classes=2, dropout_prob=0)
|
| ckpt = torch.load("exp1-2/densenet_keep/patch1.pt", map_location="cpu")
|
| model.load_state_dict(ckpt["model_state_dict"])
|
|
|
| # Demographics model (exp3)
|
| from MedMamba.MedMamba import VSSM_Double_addDemothen2
|
| model = VSSM_Double_addDemothen2(num_classes=2, demo_size=5) # e.g. sexage -> 2+3=5
|
| ckpt = torch.load("exp3/sexage.pt", map_location="cpu")
|
| model.load_state_dict(ckpt["model_state_dict"])
|
| ```
|
|
|
| ### Mapping from HF paths to original training paths
|
|
|
| Weights in JAMA_codes were trained on ORCD. Weights marked with (*) were trained by Chi-Yu and uploaded directly — no JAMA_codes equivalent.
|
|
|
| | HF path | Original training path |
|
| |---------|----------------------|
|
| | `exp0/{dataset}/{model}.pt` | Trained by Chi-Yu |
|
| | `exp0-2/swinTF_random.pt` | Trained by Chi-Yu |
|
| | `exp1-1/mamba_keep/patch{N}.pt` | Trained by Chi-Yu |
|
| | `exp1-1/mamba_remove/patch{N}.pt` | `JAMA_codes/exp1-1/Rand123/Rand123_patchidx{N}_exp1-1_model_aucbest.pt` |
|
| | `exp1-2/densenet_keep/patch{N}.pt` | `JAMA_codes/densenet_keep/Rand123/Rand123_patchidx{N}_densenet_keep_model_aucbest.pt` |
|
| | `exp1-2/densenet_remove/patch{N}.pt` | Trained by Chi-Yu |
|
| | `exp1-2/swinTF_keep/patch{N}.pt` | `JAMA_codes/swinTF_keep/Rand123/Rand123_patchidx{N}_swinTF_keep_model_aucbest.pt` |
|
| | `exp1-2/swinTF_remove/patch{N}.pt` | `JAMA_codes/swinTF_remove/Rand123/Rand123_patchidx{N}_swinTF_remove_model_aucbest.pt` |
|
| | `exp2/mamba_{N}.pt` | Trained by Chi-Yu |
|
| | `exp2-1/densenet_{N}.pt` | Trained by Chi-Yu |
|
| | `exp2-2/swinTF_{N}.pt` | Trained by Chi-Yu |
|
| | `exp3/{combo}.pt` | `JAMA_codes/{combo}_weights/mamba/sunday/Rand123/Rand123_{combo}_mamba_sunday_model_aucbest.pt` |
|
| | `exp3-1/{combo}.pt` | `JAMA_codes/{combo}_weights/densenet/sunday/Rand123/Rand123_{combo}_densenet_sunday_model_aucbest.pt` |
|
| | `exp3-2/{combo}.pt` | `JAMA_codes/{combo}_weights/swin/sunday/Rand123/Rand123_{combo}_swin_sunday_model_aucbest.pt` |
|
| | `exp4/{highpass,lowpass}/{F}Hz.pt` | `JAMA_codes/{F}{HighPass,LowPass}_weights/mamba/{direction}/Rand123/...` |
|
| | `exp4-1/{highpass,lowpass}/{F}Hz.pt` | `JAMA_codes/{F}{HighPass,LowPass}_swin_freq/Rand123/...` |
|
| | `exp4-2/{highpass,lowpass}/{F}Hz.pt` | `JAMA_codes/{F}{HighPass,LowPass}_densenet_freq/Rand123/...` |
|
| | `exp2_medgemma/mamba_{N}.pt` | `JAMA_codes_medgemma/{N}_mg_exp2/mamba/Rand123/...` |
|
| | `exp2-1_medgemma/densenet_{N}.pt` | `JAMA_codes_medgemma/{N}_mg_exp2/densenet/Rand123/...` |
|
| | `exp2-2_medgemma/swinTF_{N}.pt` | `JAMA_codes_medgemma/{N}_mg_exp2/swinTF/Rand123/...` |
|
| | `exp3_medgemma/{combo}.pt` | `JAMA_codes_medgemma/{combo}_mg_exp3/mamba/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_medgemma/{highpass,lowpass}/{F}Hz.pt` | `JAMA_codes_medgemma/{F}{HighPass,LowPass}_mg_exp4/mamba/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 Status
|
|
|
| All 153 original bootstrap evaluations complete (n_bootstrap=20, sample_size=1000, seed=42).
|
|
|
| | Experiment | Evaluations | Status |
|
| |-----------|-------------|--------|
|
| | **exp0** | 6 (3 models x 2 datasets) | Done |
|
| | **exp0-2** | 1 | Done |
|
| | **exp1-1** | 18 (mamba keep + remove x 9 patches) | Done |
|
| | **exp1-2** | 36 (densenet keep/remove + swinTF keep/remove x 9 patches) | Done |
|
| | **exp2/2-1/2-2** | 23 (8+8+7 resolutions) | Done |
|
| | **exp3/3-1/3-2** | 21 (3 models x 7 combos) | Done |
|
| | **exp4/4-1/4-2** | 48 (3 models x 2 directions x 8 freqs) | Done |
|
|
|
| ### MedGemma Bootstrap Status
|
|
|
| | Experiment | Evaluations | Status |
|
| |-----------|-------------|--------|
|
| | **MedGemma exp2/2-1/2-2** | 23 (8+8+7 resolutions) | Done |
|
| | **MedGemma exp3/3-1/3-2** | 21 (3 models x 7 combos) | Done |
|
| | **MedGemma exp4/4-1/4-2** | 48 (3 models x 2 directions x 8 freqs) | Done |
|
|
|
| Results: `bootstrap_results/` in the [code repository](https://github.com/altis5526/Insurance-Project-Journal-version-).
|
|
|
| ## Training Details
|
|
|
| - **Dataset (original)**: MIMIC-CXR-JPG v2.0.0 (normal frontal chest X-rays)
|
| - **Dataset (MedGemma)**: mimic-cxr-gemma (MedGemma-refined, normal CXRs only)
|
| - **Task**: Binary classification — Private vs. Public/Government insurance
|
| - **Image size**: 448 x 448
|
| - **Seed**: 123
|
| - **Selection**: Best validation AUC checkpoint
|
|
|
| ## Citation
|
|
|
| ```bibtex
|
| @inproceedings{chen2025unawareness,
|
| title={The Unawareness of AI Looking for Health Insurance Type from Normal Chest X-ray Images},
|
| author={Chen, Chi-Yu and others},
|
| year={2025}
|
| }
|
| ```
|
|
|
| ## Code
|
|
|
| [Code Repository](https://github.com/altis5526/Insurance-Project-Journal-version-) <!-- Update with actual repo URL -->
|
|
|
