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Found 5 papers, 2 papers with code
Many tasks make light work: Learning to localise medical anomalies from multiple synthetic tasks
There is a growing interest in single-class modelling and out-of-distribution detection as fully supervised machine learning models cannot reliably identify classes not included in their training.
Zero-Shot Anomaly Detection with Pre-trained Segmentation Models
This technical report outlines our submission to the zero-shot track of the Visual Anomaly and Novelty Detection (VAND) 2023 Challenge.
Image To Tree with Recursive Prompting
Extracting complex structures from grid-based data is a common key step in automated medical image analysis.
Atlas-ISTN: Joint Segmentation, Registration and Atlas Construction with Image-and-Spatial Transformer Networks
We propose Atlas-ISTN, a framework that jointly learns segmentation and registration on 2D and 3D image data, and constructs a population-derived atlas in the process.
Detecting Outliers with Foreign Patch Interpolation
A wide residual encoder decoder is trained to give a pixel-wise prediction of the patch and its interpolation factor.
