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 (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

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

Download a single experiment

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

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.

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

@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

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Paper for InsurancePrediction/insurance_public_weights

Algorithms Trained on Normal Chest X-rays Can Predict Health Insurance Types

Paper • 2511.11030 • Published Nov 14, 2025