Simple study on ViT performance in medical image classification
PyTorch Implementation of Lung Swapping Autoencoder
Tool that uses data augmentation for training CNN models specialized in multi-label classification of thorax anomalies in X-ray images.
The code is modified based on https://github.com/quark0/darts and used for learning
A sample subset of the NIH Chest X-ray Dataset. At only 2.4% of the size of the original dataset, it allows creating an accurate classifier using the Augmented Chest X-Ray repository.
Optimization of CheXNet in PyTorch with Intel OpenVINO
A datasheet for the ChestX-ray8 dataset, a.k.a. ChestX-ray14
Exploring data visualization with Facets and Streamlit
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