backup: 2026-08-29 06:59

implresearch.org

@@ -276,6 +276,54 @@ All are image datasets. No standard noisy-label tabular benchmark exists.
 }
 #+end_src
 
+** NIH ChestX-ray14 Experiment — Citations
+
+Approach: frozen pretrained DenseNet-121 (TorchXRayVision) as feature extractor → 1024-dim
+embeddings stored to disk → train MLP classifier using existing infrastructure. Justified by:
+
+1. *TorchXRayVision* (Cohen et al., MIDL 2022, arXiv:2111.00595) — open library with
+   pretrained DenseNet-121 weights trained on NIH ChestX-ray14 + CheXpert + MIMIC-CXR.
+   Explicitly designed for feature extraction / transfer learning. pip-installable.
+   GitHub: https://github.com/mlmed/torchxrayvision
+   Key quote: "pre-trained models can also be used as features extractors for semi-supervised
+   training or transfer learning."
+
+2. *Transfer learning with chest X-rays for ER patient classification* (Scientific Reports 2020,
+   https://www.nature.com/articles/s41598-020-78060-4) — uses frozen CheXNet output as features
+   for downstream classification. Directly precedents our approach.
+
+BibTeX:
+#+begin_src bibtex
+@inproceedings{cohen2022torchxrayvision,
+  title={{TorchXRayVision}: A library of chest {X}-ray datasets and models},
+  author={Cohen, Joseph Paul and Viviano, Joseph D. and Bertin, Paul and Morrison,
+          Paul and Torabian, Parisa and Guarrera, Matteo and Lungren, Matthew P. and
+          Chaudhari, Akshay and Brooks, Rupert and Hashir, Mohammad and Bertrand, Hadrien},
+  booktitle={Medical Imaging with Deep Learning},
+  year={2022},
+  url={https://arxiv.org/abs/2111.00595}
+}
+
+@article{bolourani2021transfer,
+  title={Transfer learning with chest {X}-rays for {ER} patient classification},
+  author={Bolourani, Sina and Brennan, Michael and Wang, Ping and Panjeton, Gregg and
+          Naaraayan, Arun and Mukherjee, Vikramjit and Bhargava, Mohit and Schreiber,
+          Theodore L. and Bhartia, Anubhav},
+  journal={Scientific Reports},
+  volume={11},
+  pages={21906},
+  year={2021},
+  doi={10.1038/s41598-020-78060-4}
+}
+#+end_src
+
+Practical notes:
+- Dataset: 42GB, download to beacon-wsl (916GB free). Mac at 95% capacity, insufficient.
+- Feature extraction: one GPU pass after food101n finishes (~3.5 days from 2026-07-28)
+- Embeddings: ~450MB (112K × 1024 × float32), preload into RAM like tabular data
+- Binary task: one pathology vs rest (e.g. Effusion — highest prevalence ~12%, IR ~7)
+- Label noise: NLP-extracted from radiology reports, estimated ~20% noise rate
+
 ** Paper Subnodes
 
 - [[id:bf0fc08a-e806-48df-b188-7a2c4c41c693][impl/paper-tabpfn]] — TabPFN: in-context learning for small tabular classification (ICLR 2023)