# DenseNet121 from JF Healthcare for the CheXpert competition model = xrv. DenseNet( weights = "densenet121-res224-mimic_ch") # MIMIC-CXR (MIT) # 512x512 models model = xrv. DenseNet( weights = "densenet121-res224-mimic_nb") # MIMIC-CXR (MIT) model = xrv. DenseNet( weights = "densenet121-res224-chex") # CheXpert (Stanford) model = xrv. DenseNet( weights = "densenet121-res224-pc") # PadChest (University of Alicante) model = xrv. DenseNet( weights = "densenet121-res224-nih") # NIH chest X-ray8 model = xrv. DenseNet( weights = "densenet121-res224-rsna") # RSNA Pneumonia Challenge model = xrv. DenseNet( weights = "densenet121-res224-all") 'Enlarged Cardiomediastinum': 0.27218717}Ī sample script to process images usings pretrained models is process_image.py Outputs = model( img) # or model.features(img) # Print results dict( zip( model.
# Load model and process image model = xrv. mean( 2) # Make single color channel transform = torchvision. normalize( img, 255) # convert 8-bit image to range img = img. Import torchxrayvision as xrv import skimage, torch, torchvision # Prepare the image: img = skimage. These datasets can also be merged and filtered to construct specific distributional shifts for studying generalization. TorchXRayVision provides access to many datasets in a uniform way so that they can be swapped out with a single line of code. Metadata associated with each dataset can vary greatly which makes it difficult to apply methods to multiple datasets. In the case of researchers developing algorithms it is important to robustly evaluate models using multiple external datasets.To address this, TorchXRayVision provides pre-trained models which are trained on large cohorts of data and enables 1) rapid analysis of large datasets 2) feature reuse for few-shot learning. In the case of researchers addressing clinical questions it is a waste of time for them to train models from scratch.
In addition, a number of classification and representation learning models with different architectures, trained on different data combinations, are available through the library to serve as baselines or feature extractors. It provides a common interface and common pre-processing chain for a wide set of publicly available chest X-ray datasets.
XRAY VISION SOFTWARE SOFTWARE
TorchXRayVision is an open source software library for working with chest X-ray datasets and deep learning models. A library for chest X-ray datasets and models.
0 kommentar(er)
