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Found 3 papers, 2 papers with code
Anatomy-aware Self-supervised Learning for Anomaly Detection in Chest Radiographs
Large numbers of labeled medical images are essential for the accurate detection of anomalies, but manual annotation is labor-intensive and time-consuming.
Weak Supervision in Convolutional Neural Network for Semantic Segmentation of Diffuse Lung Diseases Using Partially Annotated Dataset
In this paper, we propose a new weak supervision technique that effectively utilizes partially annotated dataset.
Automated Segmentation of Hip and Thigh Muscles in Metal Artifact-Contaminated CT using Convolutional Neural Network-Enhanced Normalized Metal Artifact Reduction
Our goal was to develop an automated segmentation method of the bones and muscles in the postoperative CT images.
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