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InsurancePrediction
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insurance_paper_weights

medical-imaging
chest-x-ray
insurance-prediction
fairness
bias-detection
mimic-cxr
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Add insurance paper weights

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  1. README.md +169 -0
  2. exp1-1/patch1.pt +3 -0
  3. exp1-1/patch2.pt +3 -0
  4. exp1-1/patch3.pt +3 -0
  5. exp1-1/patch4.pt +3 -0
  6. exp1-1/patch5.pt +3 -0
  7. exp1-1/patch6.pt +3 -0
  8. exp1-1/patch7.pt +3 -0
  9. exp1-1/patch8.pt +3 -0
  10. exp1-1/patch9.pt +3 -0
  11. exp1-2/densenet_keep/patch1.pt +3 -0
  12. exp1-2/densenet_keep/patch2.pt +3 -0
  13. exp1-2/densenet_keep/patch3.pt +3 -0
  14. exp1-2/densenet_keep/patch4.pt +3 -0
  15. exp1-2/densenet_keep/patch5.pt +3 -0
  16. exp1-2/densenet_keep/patch6.pt +3 -0
  17. exp1-2/densenet_keep/patch7.pt +3 -0
  18. exp1-2/densenet_keep/patch8.pt +3 -0
  19. exp1-2/densenet_keep/patch9.pt +3 -0
  20. exp1-2/swinTF_keep/patch1.pt +3 -0
  21. exp1-2/swinTF_keep/patch2.pt +3 -0
  22. exp1-2/swinTF_keep/patch3.pt +3 -0
  23. exp1-2/swinTF_keep/patch4.pt +3 -0
  24. exp1-2/swinTF_keep/patch5.pt +3 -0
  25. exp1-2/swinTF_keep/patch6.pt +3 -0
  26. exp1-2/swinTF_keep/patch7.pt +3 -0
  27. exp1-2/swinTF_keep/patch8.pt +3 -0
  28. exp1-2/swinTF_keep/patch9.pt +3 -0
  29. exp1-2/swinTF_remove/patch1.pt +3 -0
  30. exp1-2/swinTF_remove/patch2.pt +3 -0
  31. exp1-2/swinTF_remove/patch3.pt +3 -0
  32. exp1-2/swinTF_remove/patch4.pt +3 -0
  33. exp1-2/swinTF_remove/patch5.pt +3 -0
  34. exp1-2/swinTF_remove/patch6.pt +3 -0
  35. exp1-2/swinTF_remove/patch7.pt +3 -0
  36. exp1-2/swinTF_remove/patch8.pt +3 -0
  37. exp1-2/swinTF_remove/patch9.pt +3 -0
  38. exp3-1/age.pt +3 -0
  39. exp3-1/agerace.pt +3 -0
  40. exp3-1/race.pt +3 -0
  41. exp3-1/sex.pt +3 -0
  42. exp3-1/sexage.pt +3 -0
  43. exp3-1/sexagerace.pt +3 -0
  44. exp3-1/sexrace.pt +3 -0
  45. exp3-2/age.pt +3 -0
  46. exp3-2/agerace.pt +3 -0
  47. exp3-2/race.pt +3 -0
  48. exp3-2/sex.pt +3 -0
  49. exp3-2/sexage.pt +3 -0
  50. exp3-2/sexagerace.pt +3 -0
README.md CHANGED
@@ -1,3 +1,172 @@
1
  ---
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  license: mit
 
 
 
 
 
 
 
 
 
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
  ---
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  license: mit
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+ tags:
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+ - medical-imaging
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+ - chest-x-ray
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+ - insurance-prediction
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+ - fairness
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+ - bias-detection
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+ - mimic-cxr
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+ datasets:
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+ - mimic-cxr
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  ---
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+
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+ # Insurance Paper — Pretrained Weights
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+
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+ Pretrained checkpoints for **"The Unawareness of AI Looking for Health Insurance Type from Normal Chest X-ray Images"**.
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+
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+ All models were trained on [MIMIC-CXR-JPG v2.0.0](https://physionet.org/content/mimic-cxr-jpg/2.0.0/) with seed=123.
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+
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+ ## Models
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+
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+ | Model | Architecture | Params | File size |
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+ |-------|-------------|--------|-----------|
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+ | MedMamba | VSSM_DoubleLinear / VSSM_Double_addDemothen2 | ~29M | ~115 MB |
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+ | DenseNet121 | DenseNetWithDoubleLinear / _addDemothen2 | ~8M | ~59 MB |
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+ | Swin Transformer V2 | SwinTDoubleLinear / _addDemothen2 | ~50M | ~218 MB |
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+
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+ ## Repository Structure
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+
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+ ```
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+ insurance_paper_weights/
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+ ├── exp1-1/ # Patch remove-one — MedMamba
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+ │ ├── patch1.pt ... patch9.pt
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+ │
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+ ├── exp1-2/ # Patch keep/remove — DenseNet + SwinT
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+ │ ├── densenet_keep/
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+ │ │ └── patch1.pt ... patch9.pt
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+ │ ├── swinTF_keep/
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+ │ │ └── patch1.pt ... patch9.pt
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+ │ └── swinTF_remove/
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+ │ └── patch1.pt ... patch9.pt
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+ │
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+ ├── exp3/ # 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/ # Demographics — DenseNet
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+ │ └── (same 7 files)
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+ │
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+ ├── exp3-2/ # Demographics — SwinT
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+ │ └── (same 7 files)
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+ │
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+ ├── exp4/ # Frequency filtering — MedMamba
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+ │ ├── 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
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+ │
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+ ├── exp4-1/ # Frequency filtering — SwinT
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+ │ ├── highpass/ └── lowpass/
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+ │
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+ └── exp4-2/ # Frequency filtering — DenseNet
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+ ├── highpass/ └── lowpass/
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+ ```
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+
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+ ## Checkpoint Table
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+
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+ > **Note:** Exp0 (baseline full-image) and Exp1 (baseline MIMIC-only DenseNet) weights are **not yet trained** and are not included in this repository.
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+
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+ | Experiment | Description | Model | Config | # Checkpoints | HF path | Available? |
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+ |------------|-------------|-------|--------|---------------|---------|------------|
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+ | **exp0** | Baseline (full image) | densenet, mamba, swinTF | — | 0 | — | **Not trained** |
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+ | **exp1** | Baseline MIMIC-only | densenet | — | 0 | — | **Not trained** |
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+ | **exp1-1** | Patch remove | MedMamba | 9 patches | 9 | `exp1-1/patch{1-9}.pt` | Yes |
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+ | **exp1-2** | Patch keep — DenseNet | DenseNet | 9 patches | 9 | `exp1-2/densenet_keep/patch{1-9}.pt` | Yes |
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+ | **exp1-2** | Patch keep — SwinT | SwinT | 9 patches | 9 | `exp1-2/swinTF_keep/patch{1-9}.pt` | Yes |
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+ | **exp1-2** | Patch remove — SwinT | SwinT | 9 patches | 9 | `exp1-2/swinTF_remove/patch{1-9}.pt` | Yes |
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+ | **exp3** | Demographics (addDemo) | MedMamba | 7 demo combos | 7 | `exp3/{combo}.pt` | Yes |
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+ | **exp3-1** | Demographics (addDemo) | DenseNet | 7 demo combos | 7 | `exp3-1/{combo}.pt` | Yes |
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+ | **exp3-2** | Demographics (addDemo) | SwinT | 7 demo combos | 7 | `exp3-2/{combo}.pt` | Yes |
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+ | **exp4** | Freq filtering — highpass | MedMamba | 8 frequencies | 8 | `exp4/highpass/{F}Hz.pt` | Yes |
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+ | **exp4** | Freq filtering — lowpass | MedMamba | 8 frequencies | 8 | `exp4/lowpass/{F}Hz.pt` | Yes |
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+ | **exp4-1** | Freq filtering — highpass | SwinT | 8 frequencies | 8 | `exp4-1/highpass/{F}Hz.pt` | Yes |
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+ | **exp4-1** | Freq filtering — lowpass | SwinT | 8 frequencies | 8 | `exp4-1/lowpass/{F}Hz.pt` | Yes |
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+ | **exp4-2** | Freq filtering — highpass | DenseNet | 8 frequencies | 8 | `exp4-2/highpass/{F}Hz.pt` | Yes |
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+ | **exp4-2** | Freq filtering — lowpass | DenseNet | 8 frequencies | 8 | `exp4-2/lowpass/{F}Hz.pt` | Yes |
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+
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+ **Total: 105 trained checkpoints** (exp0 and exp1 not yet trained).
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+
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+ - **Demo combos** (exp3): `sex`, `age`, `race`, `sexage`, `sexrace`, `agerace`, `sexagerace`
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+ - **Frequencies** (exp4): 1, 5, 10, 25, 50, 100, 200, 400 Hz
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+ - **Patches** (exp1): 1-9, corresponding to a 3x3 grid on 448x448 images (left-to-right, top-to-bottom)
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+
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+ ## Usage
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+
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+ ### Download all weights
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+
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+ ```bash
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+ git lfs install
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+ git clone https://huggingface.co/InsurancePrediction/insurance_paper_weights
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+ ```
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+
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+ ### Download a single experiment
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+
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+ ```bash
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+ # Using huggingface_hub
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+ from huggingface_hub import snapshot_download
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+ snapshot_download(
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+ repo_id="InsurancePrediction/insurance_paper_weights",
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+ allow_patterns="exp3-1/*",
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+ local_dir="./weights"
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+ )
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+ ```
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+
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+ ### Load a checkpoint
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+
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+ ```python
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+ import torch
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+
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+ # Base model (exp1, exp4)
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+ from model import DenseNetWithDoubleLinear
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+ model = DenseNetWithDoubleLinear(num_classes=2, dropout_prob=0)
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+ ckpt = torch.load("exp1-2/densenet_keep/patch1.pt", map_location="cpu")
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+ model.load_state_dict(ckpt["model_state_dict"])
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+
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+ # Demographics model (exp3)
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+ from MedMamba.MedMamba import VSSM_Double_addDemothen2
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+ model = VSSM_Double_addDemothen2(num_classes=2, demo_size=5) # e.g. sexage -> 2+3=5
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+ ckpt = torch.load("exp3/sexage.pt", map_location="cpu")
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+ model.load_state_dict(ckpt["model_state_dict"])
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+ ```
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+
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+ ### Mapping from HF paths to original training paths
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+
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+ | HF path | Original path in JAMA_codes |
137
+ |---------|---------------------------|
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+ | `exp1-1/patch{N}.pt` | `exp1-1/Rand123/Rand123_patchidx{N}_exp1-1_model_aucbest.pt` |
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+ | `exp1-2/densenet_keep/patch{N}.pt` | `densenet_keep/Rand123/Rand123_patchidx{N}_densenet_keep_model_aucbest.pt` |
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+ | `exp1-2/swinTF_keep/patch{N}.pt` | `swinTF_keep/Rand123/Rand123_patchidx{N}_swinTF_keep_model_aucbest.pt` |
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+ | `exp1-2/swinTF_remove/patch{N}.pt` | `swinTF_remove/Rand123/Rand123_patchidx{N}_swinTF_remove_model_aucbest.pt` |
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+ | `exp3/{combo}.pt` | `{combo}_weights/mamba/sunday/Rand123/Rand123_{combo}_mamba_sunday_model_aucbest.pt` |
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+ | `exp3-1/{combo}.pt` | `{combo}_weights/densenet/sunday/Rand123/Rand123_{combo}_densenet_sunday_model_aucbest.pt` |
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+ | `exp3-2/{combo}.pt` | `{combo}_weights/swin/sunday/Rand123/Rand123_{combo}_swin_sunday_model_aucbest.pt` |
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+ | `exp4/highpass/{F}Hz.pt` | `{F}HighPass_weights/mamba/highpass/Rand123/Rand123_{F}HighPass_mamba_highpass_model_aucbest.pt` |
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+ | `exp4/lowpass/{F}Hz.pt` | `{F}LowPass_weights/mamba/lowpass/Rand123/Rand123_{F}LowPass_mamba_lowpass_model_aucbest.pt` |
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+ | `exp4-1/highpass/{F}Hz.pt` | `{F}HighPass_swin_freq/Rand123/Rand123_{F}HighPass_swin_freq_model_aucbest.pt` |
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+ | `exp4-1/lowpass/{F}Hz.pt` | `{F}LowPass_swin_freq/Rand123/Rand123_{F}LowPass_swin_freq_model_aucbest.pt` |
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+ | `exp4-2/highpass/{F}Hz.pt` | `{F}HighPass_densenet_freq/Rand123/Rand123_{F}HighPass_densenet_freq_model_aucbest.pt` |
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+ | `exp4-2/lowpass/{F}Hz.pt` | `{F}LowPass_densenet_freq/Rand123/Rand123_{F}LowPass_densenet_freq_model_aucbest.pt` |
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+
152
+ ## Training Details
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+
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+ - **Dataset**: MIMIC-CXR-JPG v2.0.0 (normal frontal chest X-rays)
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+ - **Task**: Binary classification — Private vs. Public/Government insurance
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+ - **Image size**: 448 x 448
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+ - **Seed**: 123
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+ - **Selection**: Best validation AUC checkpoint
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+
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+ ## Citation
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+
162
+ ```bibtex
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+ @inproceedings{chen2025unawareness,
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+ title={The Unawareness of AI Looking for Health Insurance Type from Normal Chest X-ray Images},
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+ author={Chen, Chi-Yu and others},
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+ year={2025}
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+ }
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+ ```
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+
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+ ## Code
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+
172
+ [GitHub Repository](https://github.com/TODO) <!-- Update with actual repo URL -->
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