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Found 7 papers, 4 papers with code
The Effect of Counterfactuals on Reading Chest X-rays
This study evaluates the effect of counterfactual explanations on the interpretation of chest X-rays.
TorchXRayVision: A library of chest X-ray datasets and models
TorchXRayVision is an open source software library for working with chest X-ray datasets and deep learning models.
Gifsplanation via Latent Shift: A Simple Autoencoder Approach to Counterfactual Generation for Chest X-rays
We also found that the Latent Shift explanation allows a user to have more confidence in true positive predictions compared to traditional approaches (0. 15$\pm$0. 95 in a 5 point scale with p=0. 01) with only a small increase in false positive predictions (0. 04$\pm$1. 06 with p=0. 57).
Fine tuning U-Net for ultrasound image segmentation: which layers?
In this study, we investigated the effect of fine-tuning different layers of a U-Net which was trained on segmentation of natural images in breast ultrasound image segmentation.
On the limits of cross-domain generalization in automated X-ray prediction
This large scale study focuses on quantifying what X-rays diagnostic prediction tasks generalize well across multiple different datasets.
Breast lesion segmentation in ultrasound images with limited annotated data
Ultrasound (US) is one of the most commonly used imaging modalities in both diagnosis and surgical interventions due to its low-cost, safety, and non-invasive characteristic.
DeepAAA: clinically applicable and generalizable detection of abdominal aortic aneurysm using deep learning
We find that DeepAAA exceeds literature-reported performance of radiologists on incidental AAA detection.
