A
ViT-L/14
vision encoder trained from scratch on
MIMIC-CXR
chest X-ray / report pairs
using
InfoNCE contrastive learning
(image encoder vs. 6-layer bidirectional text encoder).
The encoder is intended to be used as a frozen feature extractor for downstream CXR tasks.
Downstream Evaluation (frozen encoder + linear probe)
Dataset
Mean AUC
NIH CXR-14 (14-class)
0.653
CheXpert-5 (5-class)
0.759
Files
File
Description
encoder_final.pt
Vision encoder weights at the end of training
model_best.pt
Full model (encoder + text encoder) at best val loss
Usage
import torch
from lapvqa.pretrain.model import ContrastiveModel
ckpt = torch.load("encoder_final.pt", map_location="cpu")
model = ContrastiveModel()
model.vision_encoder.load_state_dict(ckpt)
model.eval()
Citation
If you use these weights please cite MIMIC-CXR:
@article{johnson2019mimic,
title = {MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports},
author = {Johnson, Alistair EW and others},
journal = {Scientific data},
volume = {6}, pages = {317}, year = {2019}
}
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